@TECHREPORT{,
author = {Konrad, E. and Orlowska, Ewa and Pawlak, Zdzislaw},
title = {Knowledge representation systems},
journal = {ICS Research Report},
volume = {433/81},
publisher = {Institute of Computer Science, Warsaw University of Technology},
address = {Warsaw, Poland},
year = {1981},
}

@ARTICLE{,
author = {Pawlak, Zdzislaw},
title = {Information systems -- theoretical foundations},
journal = {Information Systems},
volume = {6},
pages = {205--218},
year = {1981},
}

@INPROCEEDINGS{,
author = {Konrad, E. and Orlowska, Ewa and Pawlak, Zdzislaw},
title = {On approximate concept learning},
booktitle = {Proceedings of the European Conference on AI},
conference = {European Conference on AI (EAI), Orsay, France},
year = {1982},
}

@ARTICLE{,
author = {Pawlak, Zdzislaw},
title = {Rough Sets},
journal = {International Journal of Computer and Information Sciences},
volume = {11/5},
pages = {341--356},
publisher = {Plenum Press},
address = {New York, USA},
year = {1982},
issn = {0091-7036},
}

@BOOK{,
author = {Pawlak, Zdzislaw},
title = {Information Systems. Theoretical Fundamentals, in Polish},
publisher = {Wydawnictwa Naukowo-Techniczne},
address = {Warsaw, Poland},
year = {1983},
url = {http://www.wnt.com.pl},
isbn = {83-204-0520-3},
abstract = {The book provides the reader with basic theory and results about information systems and it points out some possible applications of this idea in the field of artificial intelligence. <b>Written for: </b><br>Designers of information systems, lecturers and students interested in computer science.},
keywords = {artificial intelligence, rough sets, incomplete information, knowledge representation, data processing, set theory,},
}

@INPROCEEDINGS{,
author = {Pawlak, Zdzislaw},
title = {Rough sets and decision tables},
booktitle = {Proceedings of the 5th Symposium on Computation Theory},
conference = {International Workshop on Intelligent Information Systems (IIS\'1997), Zakopane, Poland, June, 1997},
series = {Lecture Notes in Computer Science},
volume = {208},
pages = {187--196},
publisher = {AAAI Press},
address = {Montreal},
month = {December},
year = {1984},
editor = {Skowron, Andrzej},
}

@TECHREPORT{,
author = {Pawlak, Zdzislaw},
title = {Rough sets and fuzzy sets},
publisher = {Institute of Computer Science, Polish Academy of Sciences},
address = {Warsaw, Poland},
year = {1984},
note = {Note in: Decision Support Systems, Volume 1, Issue 2, April 1985, Page 182},
}

@ARTICLE{,
author = {Orlowska, Ewa and Pawlak, Zdzislaw},
title = {Measurement and indiscernibility},
journal = {Bulletin of the Polish Academy of Sciences},
volume = {32},
pages = {617--624},
year = {1984},
}

@ARTICLE{,
author = {Pawlak, Zdzislaw},
title = {On rough sets},
journal = {Bulletin of the European Association for Theoretical Computer Science (EATCS)},
volume = {24},
pages = {94--109},
month = {Octobe},
year = {1984},
}

@ARTICLE{,
author = {Orlowska, Ewa and Pawlak, Zdzislaw},
title = {Representation of nondeterministic information},
journal = {Theoretical Computer Science},
volume = {29},
pages = {27--39},
year = {1984},
}

@ARTICLE{,
author = {Pawlak, Zdzislaw},
title = {Rough Sets and Fuzzy Sets},
journal = {Fuzzy Sets and Systems},
number = {1},
volume = {17},
pages = {99-102},
year = {1985},
}

@TECHREPORT{,
author = {Pawlak, Zdzislaw},
title = {Rough sets and some problems of artificial intelligence},
publisher = {Institute of Computer Science, Polish Academy of Sciences},
address = {Warsaw, Poland},
year = {1985},
note = {Note in: Decision Support Systems, Volume 2, Issue 3, September 1986, Page 270.},
}

@ARTICLE{,
author = {Rauszer, Cecylia M. and Pawlak, Zdzislaw},
title = {Dependency of Attributes in Information Systems},
journal = {Bulletin of the Polish Academy of Sciences},
number = {9},
volume = {33},
pages = {551--559},
publisher = {Polish Academy of Sciences},
address = {Warsaw, Poland},
year = {1985},
}

@ARTICLE{,
author = {Fibak, Jan and Pawlak, Zdzislaw and Slowinski, Krzysztof and Slowinski, Roman},
title = {Rough sets based decision algorithm for treatment of duodenal ulcer by HSV},
journal = {Bulletin of the Polish Academy of Sciences},
number = {10-12},
series = {Biological Sciences},
volume = {34},
pages = {227-246},
year = {1986},
}

@ARTICLE{,
author = {Pawlak, Zdzislaw and Wong, S.K. Michael and Ziarko, Wojciech},
title = {Rough Sets: Probabilistic versus Deterministic Approach},
journal = {International Journal of Man-Machine Studies},
number = {1},
volume = {29},
pages = {81--95},
year = {1988},
}

@TECHREPORT{,
author = {Pawlak, Zdzislaw},
title = {Uncertainty and Indiscernibility. A Rough Set Perspective},
booktitle = {Institute of Computer Science Reports 2/90},
publisher = {Warsaw University of Technology},
address = {Poland, 00-665 Warsaw, Nowowiejska 15/19},
month = {October},
year = {1990},
}

@TECHREPORT{,
author = {Pawlak, Zdzislaw},
title = {Decision Logic},
booktitle = {Institute of Computer Science Reports 1/90},
publisher = {Warsaw University of Technology},
address = {Poland, 00-665 Warsaw, Nowowiejska 15/19},
month = {October},
year = {1990},
}

@BOOK{,
author = {Pawlak, Zdzislaw},
title = {Rough Sets: Theoretical Aspects of Reasoning About Data},
publisher = {Kluwer Academic Publishers},
address = {Boston, MA, USA},
year = {1991},
}

@BOOK{,
author = {Pawlak, Zdzislaw},
title = {Rough Sets. Theoretical Aspects of Reasoning about Data},
series = {Theory and decision library. D: System theory, knowledge engineering and problem solving, Vol. 9},
publisher = {Kluwer Academic Publishers},
address = {Dordrecht, The Netherlands},
year = {1991},
url = {http://www.springer.de},
isbn = {0-7923-1472-7},
abstract = {The book provides the reader with basic theory and results about rough sets and it points out some possible applications of this idea in the field of artificial intelligence, particularly in expert systems, decision machines, machine learning and data analysis. A rough set represents a new mathematical approach to vagueness and uncertainty, the emphasis being on the discovery of patterns in data. The notion of the rough set has some overlap with that of fuzzy sets, evidence theory and other mathematical models of understanding, yet it can be viewed as an entity in its own right.The concept has proved to be very useful in many real life applications, particularly in medicine, pharmacology, industry, performance evaluation, and others.The book is addressed primarily to researchers, practitioners and students in the above areas, but workers in fields as diverse as logic, psychology and philosophy will be greatly stimulated by reading about rough sets.},
keywords = {artificial intelligence, rough sets, incomplete information, knowledge representation, data processing, set theory,},
}

@INPROCEEDINGS{,
author = {Pawlak, Zdzislaw},
title = {Rough sets. Present state and the future},
booktitle = {Proceedings: the First International Workshop on Rough Sets: State of the Art and Perspectives},
conference = {International Workshop on Rough Sets: State of the Art and Perspectives (RS\'1992), Kiekrz - Poznan, Poland, September, 1992},
pages = {51-53},
month = {September},
year = {1992},
}

@ARTICLE{,
author = {Pawlak, Zdzislaw},
title = {Concurrent versus sequential - the rough sets perspective},
journal = {Bulletin of the European Association for Theoretical Computer Science (EATCS)},
volume = {48},
pages = {178--190},
month = {Octobe},
year = {1992},
}

@TECHREPORT{,
author = {Pawlak, Zdzislaw},
title = {Anatomy of Conflicts},
booktitle = {Institute of Computer Science Report 11/92},
publisher = {Warsaw University of Technology},
address = {Poland, 00-665 Warsaw, Nowowiejska 15/19},
month = {May},
year = {1992},
}

@TECHREPORT{,
author = {Pawlak, Zdzislaw},
title = {Rough Sets - Basic Concepts},
booktitle = {Institute of Computer Science Report 13/92},
publisher = {Warsaw University of Technology},
address = {Poland, 00-665 Warsaw, Nowowiejska 15/19},
month = {June},
year = {1992},
}

@TECHREPORT{,
author = {Pawlak, Zdzislaw},
title = {Concurrent Versus Sequential the Rough Sets Perspective},
booktitle = {Institute of Computer Science Report 15/92},
publisher = {Warsaw University of Technology},
address = {Poland, 00-665 Warsaw, Nowowiejska 15/19},
month = {July},
year = {1992},
}

@TECHREPORT{,
author = {Pawlak, Zdzislaw},
title = {Rough Sets and Their Applications},
booktitle = {Institute of Computer Science Report 18/92},
publisher = {Warsaw University of Technology},
address = {Poland, 00-665 Warsaw, Nowowiejska 15/19},
month = {September},
year = {1992},
}

@TECHREPORT{,
author = {Pawlak, Zdzislaw},
title = {Wiedza z Perspektywy Zbiorow Przyblizonych},
booktitle = {Institute of Computer Science Report 23/92},
publisher = {Warsaw University of Technology},
address = {Poland, 00-665 Warsaw, Nowowiejska 15/19},
month = {October},
year = {1992},
}

@TECHREPORT{,
author = {Pawlak, Zdzislaw},
title = {Rough Sets Present State and the Future},
booktitle = {Institute of Computer Science Report 20/93},
publisher = {Warsaw University of Technology},
address = {Poland, 00-665 Warsaw, Nowowiejska 15/19},
month = {March},
year = {1993},
}

@ARTICLE{,
author = {Pawlak, Zdzislaw},
title = {Roug sets: Present state and the future},
journal = {Foundations of Computing and Decision Sciences},
number = {3-4},
volume = {18},
publisher = {Poznan University of Technology},
address = {Poznan, Poland},
year = {1993},
url = {http://www.put.poznan.pl},
issn = {0867-6356},
}

@INPROCEEDINGS{,
author = {Pawlak, Zdzislaw},
title = {Knowledge and uncertainty - A rough sets approach},
booktitle = {Proceedings: Incompleteness and Uncertainty in Information Systems; SOFTEKS Workshop on Incompleteness and Uncertainty in Information Systems},
conference = {Incompleteness and Uncertainty in Information Systems:SOFTEKS Workshop on Incompleteness and Uncertainty in Information Systems (IUIS\'1993), Montreal, Canada, 1993},
year = {1993},
}

@TECHREPORT{,
author = {Pawlak, Zdzislaw and Slowinski, Roman},
title = {Decision Analysis Using Rough Sets},
booktitle = {Institute of Computer Science Report 21/93},
publisher = {Warsaw University of Technology},
address = {Poland, 00-665 Warsaw, Nowowiejska 15/19},
month = {March},
year = {1993},
}

@TECHREPORT{,
author = {Pawlak, Zdzislaw and Skowron, Andrzej},
title = {A Rough Set Approach to Decision Rules Generation},
booktitle = {Institute of Computer Science Report 23/93},
publisher = {Warsaw University of Technology},
address = {Poland, 00-665 Warsaw, Nowowiejska 15/19},
month = {March},
year = {1993},
}

@TECHREPORT{,
author = {Pawlak, Zdzislaw and Slowinski, Roman},
title = {Rough Set Approach to Multi-Attribute Decision Analysis},
booktitle = {Institute of Computer Science Report 36/93},
publisher = {Warsaw University of Technology},
address = {Poland, 00-665 Warsaw, Nowowiejska 15/19},
month = {July},
year = {1993},
}

@TECHREPORT{,
author = {Pawlak, Zdzislaw},
title = {On Some Issues Connected With Conflict Analysis},
booktitle = {Institute of Computer Science Report 37/93},
publisher = {Warsaw University of Technology},
address = {Poland, 00-665 Warsaw, Nowowiejska 15/19},
month = {September},
year = {1993},
}

@INPROCEEDINGS{,
author = {Pawlak, Zdzislaw and Skowron, Andrzej},
title = {A rough set approach for decision rules generation},
booktitle = {Proceedings of the Workshop W12: The Management of Uncertainty in AI at the 13th IJCAI},
conference = {International Joint Conference on Artificial Intelligence (IJCAI\'1993), Chambery Savoie, France, August 30, 1993},
publisher = {Springer-Verlag},
address = {Singapore},
year = {1993},
}

@ARTICLE{,
author = {Pawlak, Zdzislaw},
title = {Anatomy of conflict},
journal = {Bulletin of the European Association for Theoretical Computer Science (EATCS)},
number = {50},
pages = {234-247},
year = {1993},
}

@INCOLLECTION{,
author = {Pawlak, Zdzislaw and Skowron, Andrzej},
title = {Rough membership functions: A tool for reasoning with uncertainty},
booktitle = {Algebraic Methods in Logic and Computer Science},
pages = {135-150},
publisher = {Banach Center Publications, Polish Academy of Sciences},
address = {Warsaw, Poland},
year = {1993},
editor = {Skowron, Andrzej and Rauszer, C.},
}

@TECHREPORT{,
author = {Pawlak, Zdzislaw},
title = {Rough Real Functions},
booktitle = {Institute of Computer Science Report 50/94},
publisher = {Warsaw University of Technology},
address = {Poland, 00-665 Warsaw, Nowowiejska 15/19},
month = {September},
year = {1994},
}

@INPROCEEDINGS{,
author = {Pawlak, Zdzislaw},
title = {Knowledge and uncertainty - A rough sets approach},
booktitle = {Rough Sets, Fuzzy Sets and Knowledge Discovery (RSKD\'93), Workshops in Computing},
conference = {International Workshop on Rough Sets and Knowledge Discovery (RSKD\'1993), Banff, Alberta, Canada, October 10-15, 1993},
pages = {34-42},
publisher = {Springer-Verlag & British Computer Society},
address = {Berlin, London},
year = {1994},
}

@INPROCEEDINGS{,
author = {Pawlak, Zdzislaw},
title = {Rough sets: Present state and further prospects},
booktitle = {Proceedings: the 3rd International Workshop on Rough Sets and Soft Computing},
conference = {International Workshop on Rough Sets and Soft Computing (RSSC\'1994), San Jose, California, USA, November 10-12, 1994},
pages = {3-5},
month = {November},
year = {1994},
}

@ARTICLE{,
author = {Pawlak, Zdzislaw and Slowinski, Roman},
title = {Decision analysis using rough sets},
journal = {International Transactions in Operational Research},
number = {1},
volume = {1},
pages = {107-114},
publisher = {Elsevier Science Inc.},
address = {Amsterdam, The Netherlands},
month = {January},
year = {1994},
abstract = {We show that the rough set theory is a useful tool for analysis of decision situations, in particular multi-criteria sorting problems. It deals with vagueness in the representation of a decision situation, caused by granularity of the representation. The rough set approach produces a set of decision rules involving a reduced number of most important criteria. It does not correct vagueness manifested in the representation; instead, the rules produced are categorized into deterministic and non-deterministic. The set of decision rules explains the decision situation and may be used to support new decisions. An example illustrates the rough set analysis of a multi-criteria sorting problem.},
keywords = {decision analysis, rough set theory (RST), vagueness, multiple criteria,},
}

@INPROCEEDINGS{,
author = {Pawlak, Zdzislaw},
title = {Rough Sets Present State and Further Prospects},
booktitle = {Soft Computing},
conference = {International Workshop on Rough Sets and Soft Computing (RSSC\'1994), San Jose, California, USA, November 10-12, 1994},
pages = {3--5},
publisher = {The Society for Computer Simulation},
address = {San Diego, U.S.A.},
month = {November},
year = {1994},
editor = {Lin, Tsau Young and Wildberger, A.M.},
isbn = {1-56555-077-3},
abstract = {The rough set concept is a new mathematical tool to reason about vagueness and uncertainty in data analysis. The starting point of the rough set philosophy is the observation that objects, phenomena, process etc. sometimes cannot be distinguished due the lack of proper knowledge (information, data) about them. This indiscernibility relation caused by imperfect data leads to vagueness of concepts defined in terms of available information. The basic idea of the rough set theory is to replaced vague concepts by pairs of precise concepts, called their lower and upper approximation. Approximations are basic operations in the discussed approach. Present state of the rough set theory is outlined in the paper and some further problems concerning the theory and its applications are briefly discussed.},
keywords = {rough sets, fuzzy sets, imprecision, v vagueness, uncertainty,},
}

@ARTICLE{,
author = {Pawlak, Zdzislaw},
title = {Rough set approach to multi--attribute decision analysis},
journal = {European Journal of Operational Research},
number = {72},
pages = {443-445},
year = {1994},
}

@TECHREPORT{,
author = {Czogala, Ernest and Mrozek, Adam and Pawlak, Zdzislaw},
title = {The Idea of a Rough Fuzzy Controller and its Application to the Stabilization of a Pendulum-Car System},
booktitle = {Institute of Computer Science Report 7/94},
publisher = {Warsaw University of Technology},
address = {Poland, 00-665 Warsaw, Nowowiejska 15/19},
month = {February},
year = {1994},
}

@TECHREPORT{,
author = {Pawlak, Zdzislaw},
title = {Hard and Soft Sets},
booktitle = {Institute of Computer Science Report 10/94},
publisher = {Warsaw University of Technology},
address = {Poland, 00-665 Warsaw, Nowowiejska 15/19},
month = {February},
year = {1994},
}

@ARTICLE{,
author = {Pawlak, Zdzislaw},
title = {Rough sets and their applications},
journal = {Microcomputer Applications},
number = {2},
volume = {13},
pages = {71-75},
year = {1994},
}

@INPROCEEDINGS{,
author = {Pawlak, Zdzislaw},
title = {An inquiry into vagueness and uncertainty},
booktitle = {Proceedings: the 3rd International Workshop on Intelligent Information Systems},
conference = {International Workshop on Intelligent Information Systems (IIS\'1994), Wigry, Poland, June, 1994},
pages = {338-359},
publisher = {Institute of Computer Science, Polish Academy of Sciences},
address = {Warsaw, Poland},
month = {June},
year = {1994},
}

@TECHREPORT{,
author = {Pawlak, Zdzislaw},
title = {Vaguenes and Uncertainty a Rough Set Perspective},
booktitle = {Institute of Computer Science Report 19/94},
publisher = {Warsaw University of Technology},
address = {Poland, 00-665 Warsaw, Nowowiejska 15/19},
month = {March},
year = {1994},
}

@TECHREPORT{,
author = {Pawlak, Zdzislaw},
title = {Rough Sets, Rough Relation and Rough Functions},
booktitle = {Institute of Computer Science Report 24/94},
publisher = {Warsaw University of Technology},
address = {Poland, 00-665 Warsaw, Nowowiejska 15/19},
month = {May},
year = {1994},
}

@INPROCEEDINGS{,
author = {Pawlak, Zdzislaw and Czogala, Ernest and Mrozek, Adam},
title = {Application of a rough fuzzy controller to the stabilization of an inverted pendulum},
booktitle = {Proceedings: the 2nd European Congress on Intelligent Techniques and Soft Computing},
conference = {European Congress on Intelligent Techniques and Soft Computing (EUFIT\'1994), Aachen, Germany, September, 1994},
pages = {1403-1406},
publisher = {Verlag Mainz},
address = {Aachen, Germany},
month = {September},
year = {1994},
}

@TECHREPORT{,
author = {Pawlak, Zdzislaw},
title = {An Inquiry into Vaguenes and Uncertainty},
booktitle = {Institute of Computer Science Report 29/94},
publisher = {Warsaw University of Technology},
address = {Poland, 00-665 Warsaw, Nowowiejska 15/19},
month = {May},
year = {1994},
}

@INPROCEEDINGS{,
author = {Pawlak, Zdzislaw},
title = {Rough Sets Present State and Further Prospects},
booktitle = {Rough Sets and Soft Computing. Conference Proceedings},
conference = {International Workshop on Rough Sets and Soft Computing (RSSC\'1994), San Jose, California, USA, November 10-12, 1994},
pages = {72--76},
publisher = {San Jose State University},
address = {San Jose, California, U.S.A.},
year = {1994},
editor = {Lin, Tsau Young},
abstract = {The rough set concept (cf. Pawlak (1982) is a new mathematical tool to reason about vagueness and uncertainty. The rough set theory bears on the assumption that in order to define a set we need initially some information (knowledge) about elements of the universe - in contrast to the classical approach where the set is uniquely defined by its elements and no additional information about elements of the set is necessary. (The information about elements can be presented, for example, in a form of an attribute-value system called also an information system). Evidently, to some elements the same information can be associated and - consequently - the elements can be indiscernible in view of the available information. Thus the indiscernibility relation is the starting point of the rough set theory. It turns out that vagueness and uncertainty are strongly related to indiscernibility and can be defined on its basis.},
}

@INBOOK{,
author = {Pawlak, Zdzislaw},
title = {Hard and soft sets},
booktitle = {Rough Sets, Fuzzy Sets and Knowledge Discovery (RSKD\'93), Workshops in Computing},
pages = {130-135},
publisher = {Springer-Verlag & British Computer Society},
address = {Berlin, London},
year = {1994},
editor = {Ziarko, Wojciech},
}

@TECHREPORT{,
author = {Czogala, Ernest and Mrozek, Adam and Pawlak, Zdzislaw},
title = {Rough Fuzzy Controller as an Approximation of Fuzzy Controller},
booktitle = {Institute of Computer Science Report 32/94},
publisher = {Warsaw University of Technology},
address = {Poland, 00-665 Warsaw, Nowowiejska 15/19},
month = {June},
year = {1994},
}

@INBOOK{,
author = {Pawlak, Zdzislaw and Skowron, Andrzej},
title = {Rough membership functions},
booktitle = {Advances in the Dempster- Shafer Theory of Evidence},
pages = {251-271},
publisher = {John Wiley & Sons, Inc.},
address = {New York, NY, USA},
year = {1994},
editor = {Yaeger, R.R. and Fedrizzi, M. and Kacprzyk, J.},
}

@TECHREPORT{,
author = {Pawlak, Zdzislaw},
title = {Rough Sets Present State and Further Prospects},
booktitle = {Institute of Computer Science Report 49/94},
publisher = {Warsaw University of Technology},
address = {Poland, 00-665 Warsaw, Nowowiejska 15/19},
month = {September},
year = {1994},
}

@ARTICLE{,
author = {Pawlak, Zdzislaw and Slowinski, Roman},
title = {Rough set approach to multi-attribute decision analysis},
journal = {European Journal of Operational Research},
number = {3},
volume = {72},
pages = {443-459},
publisher = {Elsevier Science Inc.},
address = {Amsterdam, The Netherlands},
month = {February},
year = {1994},
issn = {0377-2217},
abstract = {We review the methodology of the rough set analysis of multi-attribute decision problems. Rough set theory proved to be a useful tool for analysis of a vague description of decision situations. It answers two basic questions related to multi-attribute decision problems: one about explanation of a decision situation and, another, about prescription of some decisions basing on analysis of a decision situation. We define four classes of multi-attribute decision problems, depending on the structure of their representation, its interpretation and the kind of questions related. Then, we characterize the rough set methodology for each particular class of decision problems. We use simple practical examples to illustrate this presentation. A review of related literature is made throughout the paper.},
keywords = {decision, multiple attributes, rough set theory (RST), vagueness, sorting, conflict analysis, explanation, prescription,},
}

@TECHREPORT{,
author = {Pawlak, Zdzislaw},
title = {Rough Real Functions and Rough Controllers},
booktitle = {Institute of Computer Science Report 1/95},
publisher = {Warsaw University of Technology},
address = {Poland, 00-665 Warsaw, Nowowiejska 15/19},
month = {January},
year = {1995},
abstract = {The aim of this paper is to give some basic ideas of rough functions, i.e. functions which can be understood as approximations of real functions, based on the rough set theory.},
}

@TECHREPORT{,
author = {Pawlak, Zdzislaw},
title = {Rough Set Approach to Knowledge-Based Decision Support},
booktitle = {Institute of Computer Science Report 10/95},
publisher = {Warsaw University of Technology},
address = {Poland, 00-665 Warsaw, Nowowiejska 15/19},
month = {March},
year = {1995},
abstract = {The rough set concept is a new mathematical approach to imprecision, vagueness and uncertainty. To some extend it overlaps with fuzzy set theory and evidence theory - nevertheless the rough set theory can be viewed in its own right, as an independent discipline. Many real-life applications of the theory have proved its practical usefulness. The paper presents the basic assumptions underlying the rough set philosophy, gives its fundamental 	concepts and discusses briefly some areas of applications, in particular in decision support. Finally further problems are shortly outlined.},
}

@INCOLLECTION{,
author = {Pawlak, Zdzislaw},
title = {Rough sets: Present state and further prospects},
booktitle = {Soft Computing: Rough Sets, Fuzzy Logic, Neural Networks, Uncertainty Management, Knowledge Discovery},
pages = {78-85},
publisher = {Simulation Councils, Inc.},
address = {San Diego, CA, USA},
year = {1995},
editor = {Lin, Tsau Young and Wildberger, A.M.},
}

@ARTICLE{,
author = {Pawlak, Zdzislaw},
title = {Vagueness and Uncertainty: A Rough Set Perspective},
journal = {Computational Intelligence: An International Journal},
volume = {11},
pages = {277-232},
publisher = {Blackwell Publishing},
year = {1995},
url = {http://dblp.uni-trier.de},
issn = {0824-7935},
}

@INPROCEEDINGS{,
author = {Pawlak, Zdzislaw},
title = {Rough real functions and rough controllers},
booktitle = {Proceedings:  the Workshop on Rough Sets and Data Mining at 23rd Annual Computer Science Conference},
conference = {Workshop on Rough Sets and Data Mining at Twenty Third Annual Computer Science Conference (RSDM\'1995), Nashville TN, March, 1995},
pages = {58-64},
month = {March},
year = {1995},
}

@TECHREPORT{,
author = {Pawlak, Zdzislaw},
title = {On Some Issues Connected with Roughly Continuous Functions},
booktitle = {Institute of Computer Science Report 21/95},
publisher = {Warsaw University of Technology},
address = {Poland, 00-665 Warsaw, Nowowiejska 15/19},
year = {1995},
}

@TECHREPORT{,
author = {Pawlak, Zdzislaw},
title = {Rough Sets Present State and Further Prospects},
booktitle = {Institute of Computer Science Report 32/95},
publisher = {Warsaw University of Technology},
address = {Poland, 00-665 Warsaw, Nowowiejska 15/19},
year = {1995},
abstract = {The rough set theory is a new mathematical approach to vagueness and uncertainty. To some extend it overlaps with some other mathematical tools developed to deal with imperfect knowledge, in particular with fuzzy set theory and evidence theory - nevertheless the rough set theory can be viewed in its own rights, as an independent discipline. Many real-life applications of the theory have proved its usefulness. The paper characterizes the philosophy underlying the rough set theory, gives its rudiments and discusses briefly some areas of applications. At the end some further problems are briefly outline.},
}

@INPROCEEDINGS{,
author = {Pawlak, Zdzislaw},
title = {Rough set approach to knowledge-based decision support},
booktitle = {Towards Intelligent Decision Support. Semi-Plenary Papers: the 14th European Conference of Operations Research - 20th Anniversary of EURO},
conference = {European Conference on Operational Research (EURO\'1995), Jerusalem, Israel, July, 1995},
month = {July},
year = {1995},
abstract = {The rough set concept is a new mathematical approach to imprecision, vagueness and uncertainty. To some extend it overlaps with fuzzy set theory and evidence theory -- nevertheless the rough set theory can be viewed in its own rights, as an independent discipline. Many real-life applications of the theory have proved its practical usefulness. The paper presents the basic assumptions underlying the rough sets philosophy, gives its fundamental concepts and discusses briefly some areas of applications, in particular in decision support. Finally further problems are shortly outlined.},
}

@TECHREPORT{,
author = {Pawlak, Zdzislaw},
title = {On Rough Derivatives, Rough Integrals and Rough Differential Equations},
booktitle = {Institute of Computer Science Report 41/95},
publisher = {Warsaw University of Technology},
address = {Poland, 00-665 Warsaw, Nowowiejska 15/19},
year = {1995},
abstract = {In this paper we define rough (discrete) lower and upper representation of real functions and define and investigate some properties of these representations, such as rough continuity, rough derivatives, rough integral and rough differential equations - which can be viewed as discrete counterparts of real functions. An illustrative example of the introduced concepts is given. The presented approach can be used to synthesis and analysis of discrete dynamic system, in particular in control theory. It is also related to the qualitative reasoning methods.},
}

@INCOLLECTION{,
author = {Pawlak, Zdzislaw},
title = {On some Issues Connected with Indiscernibility},
booktitle = {Mathematical Linguistics and Related Topics},
pages = {279-283},
publisher = {Editura Academiei Romane},
address = {Bucharesti, Romania},
year = {1995},
editor = {Paun, G.},
}

@INPROCEEDINGS{,
author = {Pawlak, Zdzislaw},
title = {Rough set theory},
booktitle = {Proceedings: the 2nd Annual Joint Conference on Information Sciences},
conference = {Joint Conference on Information Sciences (JCIS\'1995), Wrightsville Beach NC, September 28-October 1, 1995},
pages = {312-314},
month = {September, October},
year = {1995},
}

@ARTICLE{,
author = {Pawlak, Zdzislaw and Grzymala-Busse, Jerzy W. and Slowinski, Roman and Ziarko, Wojciech},
title = {Rough sets},
journal = {Communications of the ACM},
number = {11},
volume = {38},
pages = {88-95},
publisher = {ACM Press},
address = {New York, NY, USA},
month = {November},
year = {1995},
issn = {0001-0782},
abstract = {Rough set theory, introduced by Zdzislaw Pawlak in the early 1980s [11, 12], is a new mathematical tool to deal with vagueness and uncertainty. This approach seems to be of fundamental importance to artificial intelligence (AI) and cognitive sciences, especially in the areas of machine learning, knowledge acquisition, decision analysis, knowledge discovery from databases, expert systems, decision support systems, inductive reasoning, and pattern recognition.},
}

@ARTICLE{,
author = {Czogala, Ernest and Mrozek, Adam and Pawlak, Zdzislaw},
title = {The idea of rough-fuzzy controller},
journal = {Fuzzy Sets and Systems},
volume = {72},
pages = {61--63},
year = {1995},
}

@INPROCEEDINGS{,
author = {Pawlak, Zdzislaw},
title = {Rough sets},
booktitle = {Proceedings of ACM : Computer Science Conference},
conference = {ACM: Computer Science Conference (ACMCS\'1995), Nashville TN, February 28-March 2, 1995},
pages = {262-264},
publisher = {ACM Press},
address = {New York, NY, USA},
year = {1995},
isbn = {0-89791-737-5},
}

@ARTICLE{,
author = {Czogala, Ernest and Mrozek, Adam and Pawlak, Zdzislaw},
title = {The idea of a rough fuzzy controller and its application to the stabilization of a pendulum-car system},
journal = {Fuzzy Sets and Systems},
number = {1},
volume = {72},
pages = {61-73},
publisher = {Elsevier Science Inc.},
address = {Amsterdam, The Netherlands},
month = {May},
year = {1995},
issn = {0165-0114},
abstract = {This paper presents the idea of a rough fuzzy controller with application to the stabilization of a pendulum-car system. The structure of such a controller based on the concept of a fuzzy controller (fuzzy logic controller) is suggested. The results of a simulation comparing the performance of both controllers are shown. From these results we infer that the performance of the proposed rough fuzzy controller is satisfactory.},
keywords = {fuzzy set, rough set, fuzzy controller, rough fuzzy controller, inverted pendulum,},
}

@INPROCEEDINGS{,
author = {Pawlak, Zdzislaw},
title = {Why rough sets?},
booktitle = {Proceedings:  the 5th IEEE International Conference on Fuzzy Systems},
conference = {IEEE International Conference on Fuzzy Systems (FUZZ-IEEE\'1996), New Orleans, Louisiana, September 8-11, 1996},
pages = {738-743},
month = {September},
year = {1996},
}

@INPROCEEDINGS{,
author = {Pawlak, Zdzislaw},
title = {Data analysis with rough sets},
booktitle = {Proceedings: CODATA \'96},
conference = {(CODATA\'1996), Tsukuba, Japan, October, 1996},
year = {1996},
}

@ARTICLE{,
author = {Pawlak, Zdzislaw},
title = {Rough Sets, Rough Relations and Rough Functions},
journal = {Fundamenta Informaticae},
number = {2-3},
volume = {27},
publisher = {IOS Press},
address = {Amsterdam, The Netherlands},
month = {August},
year = {1996},
issn = {0169-2968},
}

@INPROCEEDINGS{,
author = {Pawlak, Zdzislaw},
title = {Rough sets: Present state and perspectives},
booktitle = {Proceedings:  the Sixth International Conference, Information Processing and Management of Uncertainty in Knowledge-Based Systems},
conference = {International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems (IPMU\'1996), Granada, Spain, 1996},
pages = {1137-1145},
month = {July},
year = {1996},
}

@ARTICLE{,
author = {Pawlak, Zdzislaw},
title = {Some remarks on explanation of data and specification of processes},
journal = {Bulletin of the International Rough Set Society (IRSS)},
number = {1},
volume = {1},
pages = {1-4},
year = {1996},
}

@INPROCEEDINGS{,
author = {Pawlak, Zdzislaw},
title = {Data Versus Logic - A Rough Set View},
booktitle = {Proceedings of the Fourth International Workshop on Rough Sets, Fuzzy Sets, and Machine Discovery},
conference = {International Workshop on Rough Sets, Fuzzy Sets, and Machine Discovery (RSFD\'1996), Tokyo, Japan, November 6-8, 1996},
pages = {1-8},
publisher = {Japanese Research Group on Rough Sets},
address = {Tokyo, Japan},
month = {November},
year = {1996},
editor = {Tsumoto, Shusaku and Kobayashi, Satoshi and Yokomori, Takashi and Tanaka, Hiroshi and Nakamura, Akira},
isbn = {4-947717-01-7},
abstract = {In this paper we consider some aspects of decision rule generation methods based on rough set theory. Particularly, we propose to associate with every set of decision rules a connection graph, similar to that used in switching theory, so that sets of decision rules can be represented as a kind of a switching circuit, which depicts relations between decision rules. The connection graph can be understood as a visual representation (explanation) of relations, in data, or as a specification of a discrete dynamic system (e.g., controller, program, etc.) The connection graph can be also viewed as an alternative to the well known Petri Nets concept for concurrent systems analysis.},
keywords = {rough sets, decision rules, connection graph, Petri nets,},
}

@INPROCEEDINGS{,
author = {Pawlak, Zdzislaw and Munakata, Toshinori},
title = {Rough control: Application of rough set theory to control},
booktitle = {Proceedings:  the 4th European Congress on Intelligent Techniques and Soft Computing},
conference = {European Congress on Intelligent Techniques and Soft Computing (EUFIT\'1996), Aachen, Germany, September, 1996},
pages = {209-218},
publisher = {Verlag Mainz},
address = {Aachen, Germany},
month = {September},
year = {1996},
}

@ARTICLE{,
author = {Pawlak, Zdzislaw and Skowron, Andrzej},
title = {Helena Rasiowa and Cecylia Rauszer\'s research on logical foundations of Computer Science},
journal = {Logic, Algebra and Computer Science. Helena Rasiowa and Cecylia Rauszer in Memorian},
number = {3-4},
volume = {25},
pages = {174-184},
year = {1996},
editor = {Skowron, Andrzej},
note = {a special issue},
}

@INPROCEEDINGS{,
author = {Pawlak, Zdzislaw},
title = {Rough Sets and Data Analysis},
booktitle = {Proceedings:  the 1996 Asian Fuzzy Systems Symposium - Soft Computing in Intelligent Systems and Information Processing},
conference = {1996 Asian Fuzzy Systems Symposium - Soft Computing in Intelligent Systems and Information Processing (AFS\'1996), Kenting, Taiwan ROC, December, 1996},
pages = {1-6},
month = {December},
year = {1996},
}

@INPROCEEDINGS{,
author = {Pawlak, Zdzislaw},
title = {Rough sets and Data Mining},
booktitle = {Proceedings: the International Conference on Intelligent Processing and Manufacturing Materials},
conference = {International Conference on Intelligent Processing and Manufacturing of Materials (IPMM\'1997), Gold Coast, Australia, 1997},
pages = {1-5},
year = {1997},
}

@ARTICLE{,
author = {Pawlak, Zdzislaw},
title = {Vagueness-a rough set view},
booktitle = {Structures in Logic and Computer Science},
series = {Lecture Notes in Computer Science},
volume = {1261},
pages = {106-117},
publisher = {Springer-Verlag},
address = {Berlin, Germany},
year = {1997},
}

@ARTICLE{,
author = {Pawlak, Zdzislaw},
title = {Rough set approach to knowledge-based decision support},
journal = {European Journal of Operational Research},
number = {1},
volume = {99},
pages = {48-57},
publisher = {Elsevier Science Inc.},
address = {Amsterdam, The Netherlands},
month = {May},
year = {1997},
issn = {0377-2217},
abstract = {Rough set theory is a new approach to decision making in the presence of uncertainty and vagueness. Basic concepts of rough set theory will be outlined and its possible application will be briefly discussed. Further research problems will conclude the paper.},
keywords = {rough sets, fuzzy sets, decision support,},
}

@INPROCEEDINGS{,
author = {Pawlak, Zdzislaw},
title = {Conflict analysis},
booktitle = {Proceedings of the 5th European Congress on Intelligent Techniques and Soft Computing (EUFIT\'97)},
conference = {European Congress on Intelligent Techniques and Soft Computing (EUFIT\'1997), Aachen, Germany, September 9-11, 1997},
pages = {1589-1591},
publisher = {Verlag Mainz},
address = {Aachen, Germany},
year = {1997},
}

@INCOLLECTION{,
author = {Pawlak, Zdzislaw},
title = {Rough real functions and rough controllers},
booktitle = {Rough Sets and Data Mining. Analysis of Imprecise Data},
pages = {139-147},
publisher = {Kluwer Academic Publishers},
address = {Dordrecht, The Netherlands},
year = {1997},
editor = {Lin, Tsau Young and Cercone, N.},
}

@ARTICLE{,
author = {Pawlak, Zdzislaw},
title = {Rough set approach to knowledge-based decision support},
journal = {European Journal of Operational Research},
number = {3},
volume = {29},
pages = {1-10},
year = {1997},
}

@ARTICLE{,
author = {Pawlak, Zdzislaw and Jackson, A.G. and LeClair, S.R.},
title = {Rough sets and the discovery of new materials},
journal = {Journal of Alloys and Compounds},
pages = {1-28},
year = {1997},
}

@INPROCEEDINGS{,
author = {Pawlak, Zdzislaw},
title = {Rough sets and their applications},
booktitle = {Proceedings:  Fuzzy Sets 97},
conference = {Fuzzy Sets (FS\'1997), Dortmund, Germany, 1997},
year = {1997},
}

@INCOLLECTION{,
author = {Pawlak, Zdzislaw},
title = {Rough sets},
booktitle = {Rough Sets and Data Mining. Analysis of Imprecise Data},
pages = {3-8},
publisher = {Kluwer Academic Publishers},
address = {Dordrecht, The Netherlands},
year = {1997},
editor = {Lin, Tsau Young and Cercone, N.},
}

@INCOLLECTION{,
author = {Pawlak, Zdzislaw},
title = {Rough Real Functions and Rough Controllers},
booktitle = {Rough Sets and Data Mining. Analysis of Imprecise Data},
pages = {139--147},
publisher = {Kluwer Academic Publishers},
address = {Boston, MA, USA},
year = {1997},
editor = {Lin, Tsau Young and Cercone, N.},
isbn = {0-7923-9807-6},
}

@INPROCEEDINGS{,
author = {Pawlak, Zdzislaw},
title = {Rough Modus Ponens},
booktitle = {Proceedings: the 7th Conference on Information Processing and Management of Uncertaintu in Knowledge Based Systems (IPMU\'98)},
conference = {International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems (IPMU\'1998), La Sorbonne, Paris, France, July 6-10, 1998},
pages = {1162-1165},
month = {July},
year = {1998},
}

@INCOLLECTION{,
author = {Pawlak, Zdzislaw},
title = {Rough Set Elementars},
booktitle = {Rough Sets in Knowledge Discovery 1. Methodology and Applications},
series = {Studies in Fuzziness and Soft Computing},
pages = {10--30},
publisher = {Physica-Verlag},
address = {Heidelberg, Germany},
year = {1998},
editor = {Polkowski, Lech and Skowron, Andrzej},
isbn = {3-7908-1119-X},
}

@INPROCEEDINGS{,
author = {Pawlak, Zdzislaw},
title = {Sets, fuzzy sets, and rough sets},
booktitle = {Proceedings: Fuzzy-Neuro Systems - Computational Intelligence},
conference = {IEEE International Conference on Fuzzy Systems (FUZZ-IEEE\'1996), New Orleans, Louisiana, September 8-11, 1996},
pages = {1-9},
month = {March 18-20},
year = {1998},
}

@INPROCEEDINGS{,
author = {Pawlak, Zdzislaw},
title = {Granularity of knowledge, indiscernibility, and rough sets},
booktitle = {Proceedings: IEEE Conference on Evolutionary Computation},
conference = {IEEE Conference on Evolutionary Computation (EC\'1998), Anchorage, Alaska, May 5-9, 1998},
pages = {106-110},
month = {May 5-9},
year = {1998},
}

@ARTICLE{,
author = {Jackson, A.G. and Pawlak, Zdzislaw and LeClair, S.R.},
title = {Rough sets applied to the discovery of materials knowledge},
journal = {Journal of Alloys and Compounds},
number = {1},
volume = {279},
pages = {14-21},
publisher = {Elsevier Science Inc.},
address = {Amsterdam, The Netherlands},
month = {September 4},
year = {1998},
abstract = {The functional mapping of material structure to properties, processing, and use is the principal driver for all scientific and engineering endeavors. Given the high cost of experimentation and the computational intractability of ab initio materials research, more efficient and accurate predictions of yet-to-be-made materials is an equally prominent endeavor, if not a preeminent materials research frontier. Because of the vast amounts of information to be considered in the pursuit of either, the automation of search-based methods for augmenting more analytic approaches is receiving increasing attention. Given the computational challenges to automation and to retrieving data from complex databases, search-based methods offer an expeditious approach to providing a researcher both insight and perspective. Rough sets is discussed relative to these objectives, as is current research to address its limitations and difficulties in application. Several materials related examples are offered to illustrate the application of the method.},
keywords = {material design, rough sets,},
}

@ARTICLE{,
author = {Pawlak, Zdzislaw},
title = {An inquiry into anatomy of conflicts},
journal = {Information Sciences: An International Journal},
number = {1-4},
volume = {109},
pages = {65-78},
publisher = {Elsevier Science Inc.},
address = {Amsterdam, The Netherlands},
month = {August},
year = {1998},
issn = {0020-0255},
abstract = {Conflict analysis and resolution play an important role in business, governmental, political and lawsuit disputes, labor-management negotiations, military operations and others. Many mathematical models of conflict situations have been proposed and investigated. In this paper a novel approach to conflict analysis, based on rough set theory, is outlined. Basic concepts of this approach are defined and analyzed. An illustration of the introduced concepts by the Middle East conflict is presented.},
keywords = {conflict analysis, conflict resolution, decision analysis, rough sets,},
}

@INPROCEEDINGS{,
author = {Pawlak, Zdzislaw},
title = {Reasoning about Data - A Rough Set Perspective},
booktitle = {Rough Sets and Current Trends in Computing},
conference = {International Conference on Rough Sets and Current Trends in Computing (RSCTC\'1998), Warsaw, Poland, June 22-26, 1998},
series = {Lecture Notes in Computer Science},
volume = {1424},
pages = {25--34},
publisher = {Springer-Verlag},
address = {Heidelberg, Germany},
month = {January},
year = {1998},
editor = {Polkowski, Lech and Skowron, Andrzej},
isbn = {3-540-64655-8},
issn = {0302-9743},
abstract = {The paper contains some considerations concerning the relationship between decision rules and inference rules from the rough set theory perspective. It is shown that decision rules can be interpreted as a generalization of the modus ponens inference rule, however there is an essential difference between these two concepts. Decision rules in the rough set approach are used to describe dependencies in data, whereas modus ponens is used in general to derive conclusions from premises.},
}

@ARTICLE{,
author = {Pawlak, Zdzislaw},
title = {Rough sets theory and its applications to data analysis},
journal = {Cybernetics and Systems},
number = {29},
pages = {661-688},
year = {1998},
}

@INPROCEEDINGS{,
author = {Pawlak, Zdzislaw},
title = {Sets, fuzzy sets and rough sets},
booktitle = {Artificial Intelligence},
conference = {Fuzzy-Neuro Systems (FNS\'1998), Monachium, Germany, March 18-20, 1998},
pages = {1--9},
year = {1998},
editor = {Freksa, C.},
}

@INBOOK{,
author = {Komorowski, Henryk Jan and Pawlak, Zdzislaw and Polkowski, Lech T. and Skowron, Andrzej},
title = {Rough Sets: A Tutorial},
booktitle = {Rough Fuzzy Hybridization. A New Trend in Decision Making},
pages = {3-98},
publisher = {Springer-Verlag},
address = {Singapore},
year = {1999},
editor = {Pal, Sankar Kumar and Skowron, Andrzej},
abstract = {A rapid growth of interest in rough set theory [290] and its applications can be lately seen in the number of international workshops, conferences and seminars that are either directly dedicated to rough sets, include the subject in their programs, or simply accept papers that use this approach to solve problems at hand. A large number of high quality papers on various aspects of rough sets and their applications have been published in recent years as a result of this attention. The theory has been followed by the development of several software systems that implement rough set operations. In Section 12 we present a list of software systems based on rough sets. Some of the toolkits, provide advanced graphical environments that support the process of developing and validating rough set classifiers. Rough sets are applied in many domains, such as, for instance, medicine, finance, telecommunication, vibration analysis, conflict resolution, intelligent agents, image analysis, pattern recognition, control theory, process industry, marketing, etc. Several applications have revealed the need to extend the traditional rough set approach. A special place among various extensions is taken by the approach that replaces indescernibility relation based on equivalence with a tolerance relation. In view of many generalizations, variants and extensions of rough sets a uniform presentation of the theory and methodology is in place. This tutorial paper is intended to fullfill these needs. It introduces basic notions and illustrates them with simple examples. It discusses methodologies for analysing data and surveys applications. It also presents and introduction to logical, algebraic and topological aspects, major extensions to standard rough sets, and it finally glances at future research.},
keywords = {approximate reasoning, soft computing, indiscernibility, lower and upper approximations, rough sets, boundary region, positive region, rough membership function, decision rules, dependencies to a degree, patterns, feature extraction and selection,},
}

@INPROCEEDINGS{,
author = {Pawlak, Zdzislaw},
title = {Data Mining - a rough set perspective},
booktitle = {Proceedings: Methodologies for Knowledge Discovery and Data Mining. The 3rd Pacific-Asia Conference},
conference = {Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD\'1999), Beijing, China, April, 1999},
volume = {1574},
pages = {3-11},
publisher = {Springer-Verlag},
address = {Heidelberg, Germany},
month = {January},
year = {1999},
issn = {0302-9743},
abstract = {Data mining (DM) can be perceived as a methodology for discovering hidden patterns in data. DM is a relatively new area of research and applications, stretching, over many domains like statistics, machine learning, fuzzy sets, rough sets, cluster analysis, genetics algorithms, neural networks and others. Despite many various techniques employed in DM yet it can be seen as a distinct discipline with its own problems and aims.},
}

@INBOOK{,
author = {Pawlak, Zdzislaw},
title = {Logic, Probability and Rough Sets},
booktitle = {Jewels are Forever. Contributions to Theoretical Computer Science in Honor of Arto Salomaa},
pages = {364-373},
publisher = {Springer-Verlag},
address = {Berlin, Germany},
year = {1999},
editor = {Karhumaki, J. and Maurer, H. and Paun, G. and Rozenberg, G.},
}

@INPROCEEDINGS{,
author = {Pawlak, Zdzislaw},
title = {Decision rules, Bayes\' rule, and rough sets},
booktitle = {Proceedings of the 7th International Workshop on rough Sets, Fuzzy Sets, Data Mining and Granular-Soft computing (RSFSGrC\'99)},
conference = {Internatinal Workshop on Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing (RSFDGrC\'1999), Ube-Yamaguchi, Japan, November 9-11, 1999},
series = {Lecture Notes in Artificial Intelligence},
volume = {1711},
pages = {1-9},
publisher = {Springer-Verlag},
address = {Berlin, Germany},
year = {1999},
editor = {Zhong, Ning. Liu. Jiming and Skowron, Andrzej and Ohsuga, Setsuo},
isbn = {3-540-66645-1},
}

@INCOLLECTION{,
author = {Pawlak, Zdzislaw},
title = {Probability and Rough Sets},
booktitle = {Jewels are Forever. Contributions to Theoretical Computer Science in Honor of Arto Salomaa},
pages = {364-373},
publisher = {Springer-Verlag},
address = {Berlin, Germany},
year = {1999},
editor = {Karhumaki, J. and Maurer, H. and Paun, G. and Rozenberg, G.},
}

@INPROCEEDINGS{,
author = {Pawlak, Zdzislaw},
title = {Rough set theory for intelligent industrial applications},
booktitle = {Proceedings: the 2nd International Conference on Intelligent Processing and Manufacturing of Materials},
conference = {International Conference on Intelligent Processing and Manufacturing of Materials (IPMM\'1999), Honolulu, Hawaii, 1999},
pages = {37-44},
year = {1999},
}

@INPROCEEDINGS{,
author = {Pawlak, Zdzislaw},
title = {Granularity of knowledge, indiscernibility, and rough sets},
booktitle = {Proceedings: IEEE Transactions on Automatic Control 20},
pages = {100-103},
year = {1999},
}

@ARTICLE{,
author = {Pawlak, Zdzislaw},
title = {Rough classification},
journal = {International Journal of Human-Computer Studies},
number = {2},
volume = {51},
pages = {369-383},
publisher = {Elsevier Science Inc.},
address = {Amsterdam, The Netherlands},
month = {August},
year = {1999},
abstract = {This article contains a new concept of approximate analysis of data, based on the idea of a \"rough\" set. The notion of approximate (rough) description of a set is introduced and investigated. The application to medical data analysis is shown as an example.},
}

@INCOLLECTION{,
author = {Pawlak, Zdzislaw},
title = {Rough sets, rough functions and rough calculus},
booktitle = {Rough Fuzzy Hybridization: A New Trend in Decision Making},
pages = {99-109},
publisher = {Springer-Verlag},
address = {Singapore},
year = {1999},
editor = {Pal, Sankar Kumar and Skowron, Andrzej},
}

@INCOLLECTION{,
author = {Pawlak, Zdzislaw and Polkowski, Lech T. and Skowron, Andrzej},
title = {Rough Sets and Rough Logic: a KDD Perspective},
booktitle = {Rough set methods and applications: new developments in knowledge discovery in information systems},
series = {Studies in Fuzziness and Soft Computing},
pages = {583--466},
publisher = {Physica-Verlag},
address = {Heidelberg, Germany},
year = {2000},
editor = {Polkowski, Lech and Tsumoto, Shusaku and Lin, Tsau Young},
note = {This volume is dedicated to the memory of Professor Adam Mrozek},
isbn = {3-7908-1328-1},
issn = {1434-9922},
abstract = {Basic ideas of rough set theory were proposed by Zdzislaw Pawlak [90] in the early 1990\'s. In the ensuing years, we have witnessed a systematic, world-wide growth of interest in rough sets and their applications. There are numerous areas of successful applications of rough sets software systems [101]. Many interesting case studies are reported (for references see e.g., [100,101],[87] and the bibliography in these books, in particular [19],[46],[57],[132],1146]).The main goal of rough set analysis is induction of approximations of concepts. The main goal is motivated by the basic fact, constituting also the main problem of KDD, that language we may choose for knowledge description are incomplete with respect to expressibility. A fortiori, we have to describe concepts of interest (features, properties, relations etc.) known not completely but by means of their reflections (i.e., approximations) in the chosen language. The most important issues in the induction process are: construction of relevant primitive concepts from which approximation of more complex concepts are assembled, measures of inclusion and similarity (closeness) an concepts, construction of operations producing complex concepts from the primitive ones. Basic tools of rough set approach are related to concept approximations. They are defined by approximation spaces. For many application, in particular for KDD problems, it is necessaty to search for relevant approximation spaces in the large space of parameterized approximation spaces. Strategies for tuning parameters of approximation spaces are crucial for inducing concept approximations of high quality. Methods proposed in rough set approach are to general methods like feature selection, feature extraction (e.g., discretization or grouping of symbolic value), data reduction, decision rule generation, pattern extraction(templates, association rules), or decomposition of Knowledge Discovery from the perspective of KDD as w hole. This chapter shows how several aspects of the above problems are solved by the classical rough set approach and how they are approached by some recent extension to the classical theory of rough sets. We point out the role of Boolean reasoning in solving discussed problems. Rough sets induce via its methods a specific logic, which we call rough logic. We also discuss rough logic and related logics from a wider perspective of logical approach in KDD. We show some relationships between these logics and potential directions for futher research on rough logic.},
keywords = {concept, concept approximation, rough sets, rough mereology, knowledge discovery and data mining, reducts, patterns, decision and association rules, feature selection and extraction, classical deductive systems, logics for reasoning under uncertainty, rough logic (RL),},
}

@INPROCEEDINGS{,
author = {Pawlak, Zdzislaw},
title = {Rough Sets and Decision Algorithms},
booktitle = {Rough Sets and Current Trends in Computing},
conference = {International Conference on Rough Sets and Current Trends in Computing (RSCTC\'2000), Banff, Canada, October 16-19, 2000},
series = {Lecture Notes in Artificial Intelligence},
volume = {2005},
pages = {30--45},
publisher = {Springer-Verlag},
address = {Berlin, Germany},
year = {2001},
editor = {Ziarko, Wojciech and Yao, Yiyu},
isbn = {3-540-43074-1},
issn = {0302-9743},
abstract = {Rough set based data analysis starts from a data table, called an information system. The information system contains data about objects of interest characterized in terms of some attributes. Often we distinguish in the information system condition and decision attributes. Such information system is called a decision table. The decision table describes decisions in terms of conditions that must be satisfied in order to carry out the decision specified in the decision table. With every decision table a set of decision rules, called a decision algorithm can be associated. It is shown that every decision algorithm reveals some well known probabilistic properties, in particular it satisfies the Total Probability Theorem and the Bayes\' Theorem. These properties give a new method of drawing conclusions from data, without referring to prior and posterior probabilities, inherently associated with Bayesian reasoning.},
}

@ARTICLE{,
author = {Pawlak, Zdzislaw},
title = {Combining Rough Sets and Bayes\' Rule},
journal = {Computational Intelligence: An International Journal},
number = {3},
volume = {17},
pages = {401-408},
publisher = {Blackwell Publishing},
address = {United Kingdom, USA},
month = {August},
year = {2001},
url = {http://www.blackwellpublishing.com/},
issn = {0824-7935},
abstract = {In rough set theory with every decision rule two conditional probabilities, called certainty and coverage factors, are associated. These two factors are closely related with the lower and the upper approximation of a set, basic notions of rough set theory. It is shown that these two factors satisfy the Bayes\' rule. The Bayes\' rule in our case simply shows some relationship in the data, without referring to prior and posterior probabilities intrinsically associated with Bayesian inference. This relationship can be used to \"invert\" decision rules, i.e., to find reasons (explanation) for decisions thus providing inductive as well as deductive inference in our scheme.},
keywords = {Bayes\' rule, rough sets, decision rules, information system,},
}

@ARTICLE{,
author = {Pawlak, Zdzislaw},
title = {Drawing conclusions from data - The rough set way},
journal = {International Journal of Intelligent Systems},
number = {1},
volume = {16},
pages = {3-11},
publisher = {Wiley},
address = {New York, USA},
month = {December},
year = {2001},
url = {http://www3.interscience.wiley.com/cgi-bin/home},
issn = {0884-8173},
abstract = {In the rough set theory with every decision rule two conditional probabilities, called certainty and coverage factors, are associated. These two factors are closely related with the lower and the upper approximation of a set, basic notions of rough set theory. It is shown that these two factors satisfy the Bayes\' theorem. The Bayes\' theorem in our case simply shows some relationship in the data, without referring to prior and posterior probabilities intrinsically associated with Bayesian inference in our case and can be used to  inverse  decision rules, i.e., to find reasons (explanation) for decisions.},
}

@INPROCEEDINGS{,
author = {Pawlak, Zdzislaw},
title = {New Look on Bayes\' Theorem - the Rough Set Outlook},
booktitle = {Proceedings of International Workshop on Rough Set Theory and Granular Computing (RSTGC-2001)},
conference = {International Workshop on Rough Set Theory and Granular Computing (RSTGC\'2001), Matsue, Shimane, Japan, May 20-22, 2001},
series = {Bulletin of International Rough Set Society},
volume = {5},
pages = {1-8},
month = {May},
year = {2001},
editor = {Hirano, Shoji and Inuiguchi, Masachiro and Tsumoto, Shusaku},
issn = {1346-0013},
abstract = {Rough set theory offers new insight into Bayes\' theorem. The look on Bayes\' theorem offered by rough set theory is completely different to that used in the Bayesian data analysis philosophy. It does not refer either to prior or posterior probabilities, inherently associated with Bayesian reasoning, but it reveals some probabilistic structure of the data being analyzed. It states that any data set (decision table) satisfies total probability theorem and Bayes\' theorem. This property can be used directly to draw conclusions from data without referring to prior knowledge and its revision if new evidence is available. Thus in the presented approach the only source of knowledge is the data and there is no need to assume that there is any prior knowledge besides the data. We simply look what the data are telling us. Consequently we do not refer to any prior knowledge which is updated after receiving some data.},
keywords = {rough sets, Bayes\' theorem, data analysis,},
}

@INPROCEEDINGS{,
author = {Pawlak, Zdzislaw and Peters, James F. and Skowron, Andrzej and Suraj, Zbigniew and Ramanna, Sheela and Borkowski, Maciej},
title = {Rough Measures: Theory and Applications},
booktitle = {Proceedings of International Workshop on Rough Set Theory and Granular Computing (RSTGC-2001)},
conference = {International Workshop on Rough Set Theory and Granular Computing (RSTGC\'2001), Matsue, Shimane, Japan, May 20-22, 2001},
series = {Bulletin of International Rough Set Society},
volume = {5},
pages = {177-184},
month = {May},
year = {2001},
editor = {Hirano, Shoji and Inuiguchi, Masachiro and Tsumoto, Shusaku},
issn = {1346-0013},
abstract = {This paper introduces a measure defined in the context of rough sets. Rough sets theory provides a variety of set functions that can be studied relative to various measure spaces. In particular, the rough membership function is considered. The particular rough membership function given in this paper is a nonnegative set function that is additive. It is an example of a rough measure. The idea of a rough integral is revisited in the context of the discrete Choquet integral that is defined relative to a rough measure. This rough integral computers a form of ordered, weighted \"average\" of the values of a measurabe function. Rough integrals are useful in culling from a collection of active sensors with the greatest relevance in a problem-solving effort such as classification of a \"perceived\" phenomenon in the environment of an agent. The relevance of a sensor is computed using a discrete rough integral relative to a target interval. By way of practical application, an approach to fusion of homogeneous sensors is considered. The form of sensor fusion considered in this paper consists in selecting only those sensors considered relevant in solving a problem.},
keywords = {additivity, fusion, measure, measure space, rough sets, rough membership function, rough measure, rough integral,},
}

@INPROCEEDINGS{,
author = {Pawlak, Zdzislaw and Peters, James F. and Skowron, Andrzej and Suraj, Zbigniew and Ramanna, Sheela and Borkowski, Maciej},
title = {Rough Measures and Integrals: A Brief Introduction},
booktitle = {New Frontiers in Artificial Intelligence, Joint JSAI 2001 Workshop Post-Proceedings},
conference = {Joint JSAI\'2001 Workshop (JSAI\'2001), Matsue, Japan, May 20-25, 2001},
series = {Lecture Notes in Artificial Intelligence},
volume = {2253},
pages = {375-379},
publisher = {Springer-Verlag},
address = {Heidelberg, Germany},
month = {January},
year = {2001},
editor = {Terano, Takao and Nishida, Toyoaki and Namatame, Akira and Tsumoto, Shusaku and Ohsawam, Y. and Washio, Takashi},
isbn = {3-540-43070-9},
issn = {0302-9743},
abstract = {This paper introduces a measure defined in the context of rough sets. Rough set theory provides a variety of set functions that can be studied relative to various measure spaces. In particular, the rough membership function is considered. The particular rough membership function given in this paper is a non-negative set function that is additive. It is an example of a rough measure. The idea of a rough integral is revisited in the context of the discrete Choquet integral that is defined relative to a rough measure. This rough integral computes a form of ordered, weighted \"average\" of the values of a measurable function. Rough integrals are useful in culling from a collection of active sensors those sensors with the greatest relevance in a problem-solving effort such as classification of a \"perceived\" phenomenon in the environment of an agent.},
}

@INPROCEEDINGS{,
author = {Pawlak, Zdzislaw},
title = {Bayes\' Theorem Revised - The Rough Set View},
booktitle = {New Frontiers in Artificial Intelligence, Joint JSAI 2001 Workshop Post-Proceedings},
conference = {Joint JSAI\'2001 Workshop (JSAI\'2001), Matsue, Japan, May 20-25, 2001},
series = {Lecture Notes in Computer Science},
volume = {2253},
pages = {240-250},
publisher = {Springer-Verlag},
address = {Heidelberg, Germany},
month = {January},
year = {2001},
editor = {Terano, Takao and Nishida, Toyoaki and Namatame, Akira and Tsumoto, Shusaku and Ohsawa, Yukio and Washio, Takashi},
isbn = {3-540-43070-9},
abstract = {Rough set theory offers new insight into Bayes\' theorem. The look on Bayes\' theorem offered by rough set theory is completely different from that used in the Bayesian data analysis philosophy. It does not refer either to prior or posterior probabilities, inherently associated with Bayesian reasoning, but it reveals some probabilistic structure of the data being analyzed. It states that any data set (decision table) satisfies total probability theorem and Bayes\' theorem. This property can be used directly to draw conclusions from data without referring to prior knowledge and its revision if new evidence is available. Thus in the presented approach the only source of knowledge is the data and there is no need to assume that there is any prior knowledge besides the data. We simply look what the data are telling us. Consequently we do not refer to any prior knowledge which is updated after receiving some data.},
}

@INCOLLECTION{,
author = {Greco, Salvatore and Pawlak, Zdzislaw and Slowinski, Roman},
title = {Interpretation of Bayesian confirmation measures in rough set terms},
booktitle = {Methods of Artificial Intelligence - AI-METH\'2002},
pages = {137-140},
publisher = {Silesian University of Technology Press},
address = {Gliwice},
year = {2002},
editor = {Burczynski, T. and Cholewa, W. and Moczulski, W.},
}

@ARTICLE{,
author = {Pawlak, Zdzislaw},
title = {Rough sets, decision algorithms and Bayes\' theorem},
journal = {European Journal of Operational Research},
number = {1},
volume = {136},
pages = {181-189},
publisher = {Elsevier Science Inc.},
address = {Amsterdam, The Netherlands},
month = {January},
year = {2002},
issn = {0377-2217},
abstract = {Rough set-based data analysis starts from a data table, called an information system. The information system contains data about objects of interest characterized in terms of some attributes. Often we distinguish in the information system condition and decision attributes. Such information system is called a decision table. The decision table describes decisions in terms of conditions that must be satisfied in order to carry out the decision specified in the decision table. With every decision table a set of decision rules, called a decision algorithm, can be associated. It is shown that every decision algorithm reveals some well-known probabilistic properties, in particular it satisfies the total probability theorem and Bayes\' theorem. These properties give a new method of drawing conclusions from data, without referring to prior and posterior probabilities, inherently associated with Bayesian reasoning.},
keywords = {rough sets, decision analysis, decision support systems, Bayes\' theorem,},
}

@INPROCEEDINGS{,
author = {Pawlak, Zdzislaw},
title = {In Pursuit of Patterns in Data Reasoning from Data - The Rough Set Way},
booktitle = {Rough Sets and Current Trends in Computing},
conference = {International Conference on Rough Sets and Current Trends in Computing (RSCTC\'2002), Malvern, PA, USA, October 14-16, 2002},
series = {Lecture Notes in Arificial Intelligence},
volume = {2475},
pages = {1--9},
publisher = {Springer-Verlag},
address = {Berlin, Germany},
month = {October},
year = {2002},
editor = {Alpigini, James J. and Peters, James. F. and Skowron, Andrzej and Zhong, Ning. Liu. Jiming},
isbn = {3-540-44274-X},
issn = {0302-9743},
abstract = {This paper concerns some aspects of rough set based data analysis. In particular rough set look on Bayes\' formula leads to new methodology of reasoning from data and shows interesting relationship between Bayes\' theorem, rough sets and flow graphs. Three methods of flow graphs application in drawing conclusions from data are presented and examined.},
}

@INPROCEEDINGS{,
author = {Greco, Salvatore and Pawlak, Zdzislaw and Slowinski, Roman},
title = {Generalized Decision Algorithms, Rough Inference Rules, and Flow Graphs},
booktitle = {Rough Sets and Current Trends in Computing},
conference = {International Conference on Rough Sets and Current Trends in Computing (RSCTC\'2002), Malvern, PA, USA, October 14-16, 2002},
series = {Lecture Notes in Arificial Intelligence},
volume = {2475},
pages = {93--104},
publisher = {Springer-Verlag},
address = {Berlin, Germany},
month = {October},
year = {2002},
editor = {Alpigini, James J. and Peters, James. F. and Skowron, Andrzej and Zhong, Ning. Liu. Jiming},
isbn = {3-540-44274-X},
issn = {0302-9743},
abstract = {Some probabilistic properties of decision algorithms composed of \"if..., then...\" decision rules are considered. With every decision rule three probabilities are associated: the strength, the certainty and the coverage factors of the rule. It has been shown previously that the certainty and the coverage factors are linked by Bayes\' theorem. Bayes\' theorem has also been presented in a simple form employing the strength of decision rules. In this paper, we relax some conditions on the decision algorithm, in particular, a condition on mutual exclusion of decision rules, and show that the former properties still hold. We also show how the total probability theorem is related with modus ponens and modus tollens inference rules when decision rules are true in some degree of the certainty factor. Moreover, we show that under the relaxed condition, with every decision algorithm a flow graph can be associated, giving a useful interpretation of decision algorithms.},
}

@INPROCEEDINGS{,
author = {Peters, James F. and Pawlak, Zdzislaw and Skowron, Andrzej},
title = {A Rough Set Approach to Measuring Information Granules},
booktitle = {Proceedings of the 26th International Computer Software and Applications Conference on Prolonging Software Life: Development and Redevelopment},
pages = {1135-1139},
publisher = {IEEE Computer Society Press},
address = {Washington, DC, USA},
month = {August},
year = {2002},
isbn = {0-7695-1727-7},
abstract = {This article introduces an approach to measures of information granules based on rough set theory. Informally, an information granule is a representation of a multiset (or bag) of real-world objects that are somehow indistinguishable (e.g., water samples taken from the same source at approximately the same time), or similar (e.g., various renditions of Chopin\'s sonatas or various series of very high, tinkling trills common in the songs of winter wrens), or which cause the same functionality (e.g., unmanned helicopters, line-crawling robots). Examples of measures of information granules based on rough set theory are inclusion, closeness, size, and enclosure. All of these measures are based on rough inclusion. This paper is limited to aconsideration of measures of inclusion based on a straightforward extension of classical rough membership functions and closeness based on measurement of separation of equivalence classes in a partition of the universe containing information granules. Measurement of sensor-based information granules has been motivated by recent studies of sensor signals. Asensor signal is a non-empty, finite set of sample sensor signal values temporally ordered.Classification of sensor signals requires measurements of sample signal values over subintervals of time. The contribution of this article is the introduction of a rough set approach to measuring information granule inclusion and closeness.},
keywords = {closeness, inclusion, indistinguishability, information granule, measure,},
}

@INCOLLECTION{,
author = {Pawlak, Zdzislaw},
title = {Granularity, multi-valued logic, Bayes\' theorem and rough sets},
booktitle = {Data mining, rough sets and granular computing},
pages = {487-498},
publisher = {Elsevier Science Inc.},
address = {Amsterdam, The Netherlands},
year = {2002},
isbn = {3-7908-1461-X},
abstract = {Granularity of knowledge attracted attention of many researchers recently. This paper concerns this issue from the rough set perspective. Granularity is inherently connected with foundation of rough set theory. The concept of the rough set hinges on classification of objects of interest into similarity classes, which form elementary building blocks (atoms, granules) of knowledge. These granules are employed to define basic concepts of the theory. In the paper basic concepts of rough set theory will be defined and their granular structure will be pointed out. Next the consequences of granularity of knowledge for reasoning about imprecise concepts will be discussed. In particular the relationship between some ideas of Lukasiewicz\'s multi-valued logic, Bayes\' Theorem and rough sets will be pointed out.},
}

@INCOLLECTION{,
author = {Skowron, Andrzej and Komorowski, Henryk Jan and Pawlak, Zdzislaw and Polkowski, Lech T.},
title = {Rough sets perspective on data and knowledge},
booktitle = {Handbook of data mining and knowledge discovery},
pages = {134-149},
publisher = {Oxford University Press, Inc.},
address = {New York, NY, USA},
month = {January},
year = {2002},
isbn = {0-19-511831-6},
abstract = {Rough set theory was proposed by Zdzislaw Pawlak (1982, 1991) in the early 1980s. Since then we have witnessed a systematic, worldwide growth of interest in rough set theory and its applications. The rough set approach has been introduced to deal with vague or imprecise concepts, to derive knowledge from data, and to reason about knowledge derived from data. In the first part of this chapter we outline the basic notions of rough sets, especially those that are related to knowledge extraction from data. Searching for knowledge is usually guided by some constraints (Langley et al., 1987). A wide class of such constraints can be expressed by discernibility of objects. Knowledge derived from data by the rough set approach consists of different constructs. Among them there are reducts, which are the central construct in the rough set approach, different kinds of rules (such as decision rules or association rules), dependencies, and patterns (templates), or classifiers. The reducts are of special importance since all other constructs can be derived from different kinds of reducts using the rough set approach. Strategies for searching reducts apply Boolean (propositional) reasoning (Brown, 1990), since the constraints (e.g., constraints related to the discernibility of objects) are expressible by propositional formulas. Moreover, using Boolean reasoning, minimal description-length data models (Mitchell, 1997; Rissanen, 1978) can be induced since they correspond to constructs of Boolean functions called prime implicants (or their approximations). The second part of this chapter includes illustrative examples of applications of this general scheme to inducing from data various forms of knowledge.},
}

@INCOLLECTION{,
author = {Skowron, Andrzej and Pawlak, Zdzislaw and Komorowski, Henryk Jan and Polkowski, Lech T.},
title = {B6. A rough set perspective on data and knowledge},
booktitle = {Handbook of {KDD}},
pages = {134-149},
publisher = {Computer Science Department, University of Edinburgh},
address = {Edinburgh},
year = {2002},
editor = {Kloesgen, Willi and Zytkow, Jan},
}

@ARTICLE{,
author = {Pawlak, Zdzislaw},
title = {Rough sets and intelligent data analysis},
journal = {Information Sciences: An International Journal},
number = {1-4},
volume = {147},
pages = {1-12},
publisher = {Elsevier Science Inc.},
address = {Amsterdam, The Netherlands},
month = {November},
year = {2002},
issn = {0020-0255},
abstract = {Rough set based data analysis starts from a data table, called an information system. The information system contains data about objects of interest characterized in terms of some attributes. Often we distinguish in the information system condition and decision attributes. Such information system is called a decision table. The decision table describes decisions in terms of conditions that must be satisfied in order to carry out the decision specified in the decision table. With every decision table a set of decision rules, called a decision algorithm can be associated. It is shown that every decision algorithm reveals some well-known probabilistic properties, in particular it satisfies the total probability theorem and the Bayes\' theorem. These properties give a new method of drawing conclusions from data, without referring to prior and posterior probabilities, inherently associated with Bayesian reasoning.},
}

@INPROCEEDINGS{,
author = {Pawlak, Zdzislaw},
title = {The Rough Set View on Bayes\' Theorem},
booktitle = {Advances in Soft Computing - AFSS 2002, 2002 AFSS International Conference on Fuzzy Systems. Calcutta, India, February 3-6, 2002,Proceedings},
conference = {International Conference on Fuzzy Systems (AFSS\'2002), Calcutta, India, February 3-6, 2002},
series = {Lecture Notes in Computer Science},
volume = {2275},
pages = {106},
publisher = {Springer-Verlag},
address = {Heidelberg, Germany},
year = {2002},
editor = {Pal, Nikhil R. and Sugeno, Michio},
isbn = {3-540-43150-0},
abstract = {Rough set theory offers new perspective on Bayes\' theorem. The look on Bayes\' theorem offered by rough set theory reveals that any data set (decision table) satisfies total probability theorem and Bayes\' theorem. These properties can be used directly to draw conclusions from objective data without referring to subjective prior knowledge and its revision if new evidence is available. Thus the rough set view on Bayes\' theorem is rather objective in contrast to subjective \"classical\" interpretation of the theorem.},
}

@INPROCEEDINGS{,
author = {Pawlak, Zdzislaw},
title = {Theorize with Data Using Rough Sets},
booktitle = {Proceedings of the 26th International Computer Software and Applications Conference on Prolonging Software Life: Development and Redevelopment},
pages = {1125-1128},
publisher = {Elsevier Science Inc.},
address = {Amsterdam, The Netherlands},
year = {2002},
isbn = {0-7695-1727-7},
abstract = {In this paper we will study the granular structure of data in the language of rough set theory. It is shown That the granularity of data can be represented in a form of a flow graph, and the relationship between granules obeys Bayes\' theorem. This leads to a new method of data analysis.Motto: \\\"It is a capital mistake to theorize before one has data\\\"Sherlock Holmes\\\"In: A SCandal in BohemiaThis paper is dedicated to the renowned Mr. Sherlock Holmes for his mastery in theorizing about data.},
}

@INCOLLECTION{,
author = {Skowron, Andrzej and Komorowski, Henryk Jan and Pawlak, Zdzislaw and Polkowski, Lech T.},
title = {A rough set perspective on data and knowledge},
booktitle = {Handbook of Data Mining and Knowledge Discovery},
pages = {134--149},
publisher = {Oxford University Press},
address = {Oxford, UK},
month = {January},
year = {2002},
editor = {Kloesgen, Willi and Zytkow, Jan},
url = {http://www.oup.co.uk},
isbn = {0-19-511831-6},
abstract = {Rough set theory was proposed by Zdzislaw Pawlak (1982, 1991) in the early 1980s. Since then we have witnessed a systematic, worldwide growth of interest in rough set theory and its applications. The rough set approach has been introduced to deal with vague or imprecise concepts, to derive knowledge from data, and to reason about knowledge derived from data. In the first part of this chapter we outline the basic notions of rough sets, especially those that are related to knowledge extraction from data. Searching for knowledge is usually guided by some constraints (Langley et al., 1987). A wide class of such constraints can be expressed by discernibility of objects. Knowledge derived from data by the rough set approach consists of different constructs. Among them there are reducts, which are the central construct in the rough set approach, different kinds of rules (such as decision rules or association rules), dependencies, and patterns (templates), or classifiers. The reducts are of special importance since all other constructs can be derived from different kinds of reducts using the rough set approach. Strategies for searching reducts apply Boolean (propositional) reasoning (Brown, 1990), since the constraints (e.g., constraints related to the discernibility of objects) are expressible by propositional formulas. Moreover, using Boolean reasoning, minimal description-length data models (Mitchell, 1997; Rissanen, 1978) can be induced since they correspond to constructs of Boolean functions called prime implicants (or their approximations). The second part of this chapter includes illustrative examples of applications of this general scheme to inducing from data various forms of knowledge.},
}

@INPROCEEDINGS{,
author = {Pawlak, Zdzislaw},
title = {Flow Graphs and Decision Algorithms},
booktitle = {Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing},
conference = {Internatinal Workshop on Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing (RSFDGrC\'2003), Chongqing, China, May 26-29, 2003},
series = {Lecture Notes in Artificial Intelligence},
volume = {2639},
pages = {1--10},
publisher = {Springer-Verlag},
address = {Berlin, Germany},
month = {May},
year = {2003},
editor = {Wang, Guoyin and Liu, Quing and Yao, Yiyu and Skowron, Andrzej},
isbn = {3-540-14040-9},
issn = {0302-9743},
abstract = {In this paper we introduce a new kind of flow networks, called flow graphs, different to that proposed by Ford and Fulkerson. Flow graphs are meant to be used as a mathematical tool to analysis of information flow in decision algorithms, in contrast to material flow optimization considered in classical flow network analysis. In the proposed approach branches of the flow graph are interpreted as decision rules, while the whole flow graph can be understood as a representation of decision algorithm. The information flow in flow graphs is governed by Bayes\' rule, however, in our case, the rule does not have probabilistic meaning and is entirely deterministic. It describes simply information flow distribution in flow graphs. This property can be used to draw conclusions from data, without referring to its probabilistic structure.},
}

@INPROCEEDINGS{,
author = {Pawlak, Zdzislaw},
title = {Probability, Truth and Flow Graph},
booktitle = {Proceedings of The International Workshop on Rough Sets in Knowledge Discovery and Soft Computing},
conference = {International Workshop on Rough Sets in Knowledge Discovery and Soft Computing (RSKD\'2003), Warsaw, Poland, April 12-13, 2003},
pages = {1-9},
year = {2003},
editor = {Skowron, Andrzej and Szczuka, Marcin},
abstract = {In 1913 Jan Lukasiewicz proposed to use as mathematical foundation of probability. He claims that probability is \"purely logical concept\" and that his approach frees probability from its obscure philosophical connotation. He recommends to replace the concept of probability by the concept of a true value, which can be regarded as a degree of truth, i.e., a number between 0 and 1, of propositional functions (called in his work indefinite propositions). Further he shows that all laws of probability can be obtained from a property built logical calculus. In this paper we show that the idea of Lukasiewicz can be also expressed differently. Instead of using truth values in place of probability, stipulated by Lukasiewicz, we propose, in this paper, using of deterministic flow analysis in flow networks (graphs). In the proposed setting, flow is governed by some probabilistic rules (e. g., Bayes\' rule) or by the corresponding logical rules, proposed by Lukasiewicz, through, the formulas have entirely deterministic meaning, and need neither probabilistic nor logical interpretation. They simply describe flow distribution in flow graphs. However, flow graphs introduced here are different to those proposed by Ford and Fulkerson, for optimal flow analysis, because they model rather flow distribution in a plumbing network, then the optimal flow. The flow graphs considered in this paper can be also used as a description of a decision algorithms, where branches of the graphs are interpreted as decision rules. This feature causes that flow networks can be also used as a new tool for data analysis, and knowledge representation.},
}

@INPROCEEDINGS{,
author = {Pawlak, Zdzislaw},
title = {Decision Rules and Dependencies},
booktitle = {Proceedings of the (CS&P\'2003), Workshop},
conference = {Concurrency, Specification and Programming Workshop (CS&P\'2003), Czarna, Poland, September 25-27, 2003},
volume = {2},
pages = {35-40},
publisher = {Warsaw University},
address = {Warsaw, Poland},
year = {2003},
editor = {Czaja, Ludwik},
}

@INCOLLECTION{,
author = {Pawlak, Zdzislaw and Peters, James F. and Skowron, Andrzej and Suraj, Zbigniew and Ramanna, Sheela and Borkowski, Maciej},
title = {Rough Measures, Rough Integrals and Sensor Fusion},
booktitle = {Rough Set Theory and Granular Computing},
series = {Studies in Fuzziness and Soft Computing},
volume = {125},
pages = {263--272},
publisher = {Springer-Verlag},
address = {Berlin, Germany},
year = {2003},
editor = {Inuiguchi, Masachiro and Hirano, Shoji and Tsumoto, Shusaku},
}

@ARTICLE{,
author = {Pawlak, Zdzislaw},
title = {Some Issues on Rough Sets},
journal = {Transactions on Rough Sets I},
series = {Lecture Notes in Computer Science},
volume = {3100},
pages = {1--58},
publisher = {Springer-Verlag},
address = {Berlin, Germany},
year = {2004},
editor = {Peters, James. F. and Skowron, Andrzej and Grzmala-Busse, Jerzy W. and Kostek, B. and Swiniarski, Roman W. and Szczuka, Marcin},
url = {http://www.springer.de},
isbn = {3-540-22374-6},
issn = {0302-9743},
abstract = {The aim of this paper is to give rudiments of rough set theory and present some recent research directions proposed by the author. Rough set theory is a new mathematical approach to imperfect knowledge. The problem of imperfect knowledge has been tackled for a long time by philosophers, logicians and mathematicians. Recently it became also a crucial issue for computer scientists, particularly in the area of artificial intelligence. There are many approaches to the problem of how to understand and manipulate imperfect knowledge. The most successful one is, no doubt, the fuzzy set theory proposed by Lotfi Zadeh [1]. Rough set theory proposed by the author in [2] presents still another attempt to this problem. This theory has attracted attention of many researchers and practitioners all over the world, who have contributed essentially to its development and applications. Rough set theory overlaps with many other theories. However we will refrain to discuss these connections here. Despite this, rough set theory may be considered as an independent discipline in its own right. Rough set theory has found many interesting applications. The rough set approach seems to be of fundamental importance to AI and cognitive sciences, especially in the areas of machine learning, knowledge acquisition, decision analysis, knowledge discovery from databases, expert systems, inductive reasoning and pattern recognition.},
keywords = {rough sets, fuzzy sets, approximate reasoning, information system, decision systems, reducts, decision algorithms, conflict analysis, data analysis, flow graphs, decision networks,},
}

@INPROCEEDINGS{,
author = {Pawlak, Zdzislaw and Peters, James F. and Skowron, Andrzej},
title = {Approximating functions using rough sets},
booktitle = {Proc. North American Fuzzy Information Processing Society},
conference = {North American Fuzzy Information Processing Society (NAFI\'2004), Banff, Alberta, 2004},
pages = {358-371},
publisher = {IEEE Computer Society Press},
year = {2004},
}

@ARTICLE{,
author = {Pawlak, Zdzislaw},
title = {Decision Rules and Dependencies},
journal = {Fundamenta Informaticae},
number = {1-4},
volume = {60},
pages = {33-39},
publisher = {IOS Press},
address = {Amsterdam, The Netherlands},
month = {March},
year = {2004},
url = {http://www.iospress.nl},
issn = {0169-2968},
abstract = {We proposed in this paper to use some ideas of Jan Lukasiewicz, concerning independence of logical formulas, to study dependencies in databases.},
}

@INCOLLECTION{,
author = {Pawlak, Zdzislaw},
title = {Elementary Rough Set Granules: Toward a Rough Set Processor},
booktitle = {Rough-Neurocomputing: Techniques for Computing with Words},
series = {Cognitive Technologies},
pages = {5--14},
publisher = {Springer-Verlag},
address = {Berlin, Germany},
year = {2004},
editor = {Pal, Sankar Kumar and Polkowski, Lech and Skowron, Andrzej},
isbn = {3-540-43059-8},
issn = {1611-2482},
abstract = {In this chapter, the basics of the rough set approach are presented, and an outline of an exemplary processor structure is given. The organization of a simple processor is based on elementary rough set granules and dependencies between them. The rough set processor (RSP) is meant to be used as an additional fast classification until in ordinary computers or as an autonomous learning machine. In the latter case, the RSP can be regarded as an alternative to neutral networks.},
}

@INPROCEEDINGS{,
author = {Pawlak, Zdzislaw},
title = {Decision Networks},
booktitle = {Rough Sets and Current Trends in Computing},
conference = {International Conference on Rough Sets and Current Trends in Computing (RSCTC\'2004), Uppsala, Sweden, June 1-5, 2004},
series = {Lecture Notes in Artificial Intelligence},
volume = {3066},
pages = {1-7},
publisher = {Springer-Verlag},
address = {Berlin, Germany},
month = {June},
year = {2004},
editor = {Tsumoto, Shusaku and Slowinski, Roman W. and Komorowski, Jan and Grzmala-Busse, Jerzy W.},
isbn = {3-540-22117-4},
issn = {0302-9743},
abstract = {A decision network is a finite, directed acyclic graph, nodes of which represent logical formulas, whereas branches ? are interpreted as decision rules. Every path in the graph represents a chain of decision rules, which describe compound decision. Some properties of decision networks will be given and a simple example will illustrate the presented ideas and show possible applications.},
keywords = {decision rules, decision algorithms, decision networks,},
}

@INPROCEEDINGS{,
author = {Pawlak, Zdzislaw},
title = {Inference Rules and Decision Rules},
booktitle = {Artificial Intelligence and Soft Computing - ICAISC 2004},
conference = {International Conference on Artificial Intelligence and Soft Computing (ICAISC\'2004), Zakopane, Poland, June 7-11, 2004},
series = {Lecture Notes in Artificial Intelligence},
volume = {3070},
pages = {102--108},
publisher = {Springer-Verlag},
address = {Berlin, Germany},
year = {2004},
editor = {Rutkowski, L. and Siekmann, J. and Tadeusiewicz, R. and Zadeh, L.A.},
isbn = {978-3-540-22123-4},
issn = {0302-9743},
abstract = {In reasoning from data (data mining) we also use rules if then , called decision rules, to express our knowledge about reality, but in this case the meaning of the expression is different. It does not express general knowledge but refers to partial facts. Therefore decision rules are not true or false but probable (possible) only. In this paper we compare inference rules and decision rules in the context of decision networks, proposed by the author as a new approach to analyze reasoning patterns in data.},
keywords = {modus ponens, modus tollens, decision rules,},
}

@INPROCEEDINGS{,
author = {Greco, Salvatore and Pawlak, Zdzislaw and Slowinski, Roman},
title = {Bayesian Confirmation Measures within Rough Set Approach},
booktitle = {Rough Sets and Current Trends in Computing},
conference = {International Conference on Rough Sets and Current Trends in Computing (RSCTC\'2004), Uppsala, Sweden, June 1-5, 2004},
series = {Lecture Notes in Artificial Intelligence},
volume = {3066},
pages = {264-273},
publisher = {Springer-Verlag},
address = {Berlin, Germany},
month = {June},
year = {2004},
editor = {Tsumoto, Shusaku and Slowinski, Roman W. and Komorowski, Jan and Grzmala-Busse, Jerzy W.},
isbn = {3-540-22117-4},
issn = {0302-9743},
abstract = {Bayesian confirmation theory considers a variety of non-equivalent confirmation measures quantifying the degree to which a piece of evidence supports a hypothesis. In this paper, we apply some of the most relevant confirmation measures within the rough set approach. Moreover, we discuss interesting properties of these confirmation measures and we propose a new property of monotonicity that is particularly relevant within rough set approach. The main result of this paper states which one of the confirmation measures considered in the literature have the desirable properties from the viewpoint of the rough set approach.},
}

@INPROCEEDINGS{,
author = {Pawlak, Zdzislaw},
title = {Flow Graphs, their Fusion, and Data Analysis},
booktitle = {Monitoring, Security and Rescue Techniques in Multiagent Systems},
conference = {International Workshop (MSRAS\'2004), Plock, Poland, June 7-9, 2004},
pages = {3--4},
publisher = {Wydzial Matematyki, Informatyki i Mechaniki, Uniwersytet Warszawski},
address = {Warszawa, Poland},
year = {2004},
editor = {Jankowski, A. and Skowron, Andrzej and Szczuka, Marcin},
isbn = {83-920897-0-7},
abstract = {In [4] we presented a new approach to data analysis based on flow distribution study in a flow network, called here a flow graph. The introduced flow graph differs from that proposed by Ford and Fulkerson[1], Branches of the flow graph are interpreted as decision rules, whereas the flow graph is supposed to describe a decision algorithm. Hence, we propose to model decision processes as flow graphs and analyze decision in term of flow spreading in the graph. With every decision rule three coefficients are associated, called strength, certainty and the coverage factors. These coefficients were widely used in data mining and rough set theory, but in fact they were first introduced by Lukasiewicz [1] in connection with his study of logic and probability. These coefficients have a probabilistic flavour, but they can be also interpreted in a deterministic way, describing flow distribution in the flow graph. Moreover, these coefficients satisfy Bayes??Ă�???€???�¬Ă�???€???rule. Thus, in the presented approach Bayes??Ă�???€???�¬Ă�???€??? rule has entirely deterministic interpretation, without reference to its probabilistic nature, inherently associated with classical Bayesian philosophy. In this paper we introduce hierarchical structure of flow networks by allowing to substitute of single branch in the flow graph by another flow graph. This ??Ă�???€???�¬???€?�zooming??Ă�???€???�¬? operation can be repeated recursively, which allows us to look at data with different accuracy and move from general picture to details. A simple tutorial example will be used to illustrate the introduced ideas.},
}

@ARTICLE{,
author = {Greco, Salvatore and Pawlak, Zdzislaw and Slowinski, Roman},
title = {Can Bayesian confirmation measures be useful for rough set decision rules?},
journal = {Engineering Applications of Artificial Intelligence},
number = {4},
volume = {17},
pages = {345-361},
publication place = {Amsterdam},
publisher = {Oxford University Press, Inc.},
address = {New York, NY, USA},
month = {June},
year = {2004},
abstract = {Bayesian confirmation theory considers a variety of non-equivalent confirmation measures which say in what degree a piece of evidence confirms a hypothesis. In this paper, we apply some well-known confirmation measures within the rough set approach to discovering relationships in data in terms of decision rules. Moreover, we discuss some interesting properties of these confirmation measures and we propose a new property of monotonicity that is particularly relevant within rough set approach. The main result of this paper states that only two from among confirmation measures considered in the literature have the desirable properties from the viewpoint of the rough set approach. Moreover, we clarify relationships between logical implications and decision rules, and we compare the confirmation measures to several related measures, like independence (dependence) of logical formulas, interestingness measures in data mining and Bayesian solutions of raven&#8217;s paradox.},
keywords = {confirmation measures,},
}

@ARTICLE{,
author = {Pawlak, Zdzislaw},
title = {Flow Graphs and Data Mining},
journal = {Transactions on Rough Sets III},
volume = {3},
pages = {1--36},
publisher = {Springer-Verlag},
address = {Berlin, Germany},
year = {2005},
url = {http://www.springer.de},
isbn = {3-540-25998-8},
issn = {0302-9743},
abstract = {In this paper we propose a new approach to data mining and knowledge discovery based on information flow distribution in a flow graph. Flow graphs introduced in this paper are different from those proposed by Ford and Fulkerson for optimal flow analysis and they model flow distribution in a network rather than the optimal flow which is used for information flow examination in decision algorithms. It is revealed that flow in a flow graph is governed by Bayes\' rule, but the rule has an entirely deterministic interpretation without referring to its probabilistic roots. Besides, a decision algorithm induced by a flow graph and dependency between conditions and decisions of decision rules is introduced and studied, which is used next to simplify decision algorithms.},
keywords = {flow graph, data mining, knowledge discovery, decision algorithms,},
}

@ARTICLE{,
author = {Pawlak, Zdzislaw},
title = {Some remarks on conflict analysis},
journal = {European Journal of Operational Research},
number = {3},
volume = {166},
pages = {649-654},
publisher = {Elsevier Science Inc.},
address = {Amsterdam, The Netherlands},
month = {November},
year = {2005},
issn = {0377-2217},
abstract = {Study of conflicts is of greatest importance both practically and theoretically. Conflict analysis and resolution play an important role in business, governmental, political and lawsuits disputes, labor-management negotiations, military operations and others. Many formal models of conflict situations have been proposed and studied. In this paper we outline a new approach to conflict analysis, which will be illustrated by a simple tutorial example of voting analysis in conflict situations.},
keywords = {conflict analysis, rough sets, decision analysis,},
}

@INPROCEEDINGS{,
author = {Pawlak, Zdzislaw},
title = {Rough Sets and Flow Graphs},
booktitle = {Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing, Part I},
conference = {Internatinal Workshop on Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing (RSFDGrC\'2005), Regina, Canada, August 31-September 3, 2005},
series = {Lecture Notes in Artificial Intelligence},
volume = {3641},
pages = {1--11},
publisher = {Springer-Verlag},
address = {Berlin, Germany},
month = {August},
year = {2005},
editor = {Slezak, Dominik and Wang, Guoyin and Szczuka, Marcin and Duntsch, Ivo and Yao, Yiyu},
isbn = {3-540-28653-5},
issn = {0302-9743},
abstract = {This paper concerns the relationship between rough sets and flow graphs. It is shown that flow graph can be used both as formal language for computing approximations of sets in the sense of rough set theory, and as description tool for data structure. This description is employed next for finding patterns in data. To this end decision algorithm induced by the flow graph is defined and studied.},
keywords = {rough sets, flow graphs, decision algorithms,},
}

@INPROCEEDINGS{,
author = {Pawlak, Zdzislaw},
title = {A Treatise on Rough Sets},
booktitle = {Transactions on Rough Sets IV},
series = {Lecture Notes in Computer Science},
volume = {3700},
pages = {1--17},
publisher = {Springer-Verlag},
address = {Berlin, Germany},
year = {2005},
editor = {Peters, James F. and Skowron, Andrzej},
isbn = {3-540-29830-4},
issn = {0302-9743},
abstract = {This article presents some general remarks on rough sets and their place in general picture of research on vagueness and uncertainty - concepts of utmost interest, for many years, for philosophers, mathematicians, logicians and recently also for computer scientists and engineers particularly those working in such areas as AI, computational intelligence, intelligent systems, cognitive science, data mining and machine learning. Thus this article is intended to present some philosophical observations rather than to consider technical details or applications of rough set theory. Therefore we also refrain from presentation of many interesting applications and some generalizations of the theory.},
keywords = {sets, fuzzy sets, rough sets, antinomies, vagueness,},
}

@INPROCEEDINGS{,
author = {Pawlak, Zdzislaw},
title = {Decision Trees and Flow Graphs, invited paper},
booktitle = {Rough Sets and Current Trends in Computing},
conference = {International Conference on Rough Sets and Current Trends in Computing (RSCTC\'2006), Kobe, Japan, November 6-8, 2006},
series = {Lecture Notes in Artificial Intelligence},
volume = {4259},
pages = {1--11},
publisher = {Springer-Verlag},
address = {Berlin, Germany},
month = {November},
year = {2006},
editor = {Greco, Salvatore and Hata, Yutaka and Hirano, Shoji and Inuiguchi, Masahiro and Miyamoto, Sadaaki and Nguyen, Hung Son and Slowinski, Roman},
isbn = {978-3-540-47693-1},
issn = {0302-9743},
abstract = {We consider association of decision trees and flow graphs, resulting in a new method of decision rule generation from data, and giving a better insight in data structure. The introduced flow graphs can also give a new look at the conception of probability. We show that in some cases the conception of probability can be eliminated and replaced by a study of deterministic flows in a flow network.},
}