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E-raamat: Rough Sets in Knowledge Discovery 2: Applications, Case Studies and Software Systems

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The papers on rough set theory and its applications placed in this volume present a wide spectrum of problems representative to the present. stage of this theory. Researchers from many countries reveal their rec.ent results on various aspects of rough sets. The papers are not confined only to mathematical theory but also include algorithmic aspects, applications and information about software designed for data analysis based on this theory. The volume contains also list of selected publications on rough sets which can be very useful to every one engaged in research or applications in this domain and sometimes perhaps unaware of results of other authors. The book shows that rough set theory is a vivid and vigorous domain with serious results to its credit and bright perspective for future developments. It lays on the crossroads of fuzzy sets, theory of evidence, neural networks, Petri nets and many other branches of AI, logic and mathematics. These diverse connec­ tions seem to be a very fertile feature of rough set theory and have essentially contributed to its wide and rapid expansion. It is worth mentioning that its philosophical roots stretch down from Leibniz, Frege and Russell up to Popper. Therefore many concepts dwelled on in rough set theory are not entirely new, nevertheless the theory can be viewed as an independent discipline on its own rights. Rough set theory has found many interesting real life applications in medicine, banking, industry and others.
Foreword v Z. Pawlak L. Zadeh
Chapter
1. Introducing the Book 1(12) L. Polkowski A. Skowron PART
1. APPLICATIONS 13(134)
Chapter
2. Rough Approximation of a Preference Relation in a Pairwise Comparison Table 13(24) S. Greco B. Matarazzo R. Slowinski
Chapter
3. Learning Decision Rules from Similarity Based Rough Approximations 37(18) K. Krawiec R. Slowinski D. Vanderpooten
Chapter
4. Discovery of Data Patterns with Applications to Decomposition and Classification Problems 55(43) S. Hoa Nguyen A. Skowron P. Synak
Chapter
5. Answering Non-Standard Queries in Distributed Knowledge-Based Systems 98(11) Z.W. Ras
Chapter
6. Approximation Spaces, Reducts and Representatives 109(18) J. Stepaniuk
Chapter
7. Data Mining: A Probabilistic Rough Set Approach 127(20) N. Zhong J.Z. Dong S. Ohsuga PART 2: CASE STUDIES 147(198)
Chapter
8. Soft Processing of Audio Signals 147(19) A. Czyzewski
Chapter
9. A Rough Set Approach to Information Retrieval 166(12) K. Funakoshi T. Bao Ho
Chapter
10. Extraction Method Based on Rough Set Theory of Rule-Type Knowledge from Diagnostic Cases of Slope-Failure Danger Levels 178(15) H. Furuta M. Hirokane Y. Mikumo
Chapter
11. Soft Computing-Based Recognition of Musical Sounds 193(21) B. Kostek
Chapter
12. Rough Sets in Industrial Applications 214(24) A. Mrozek L. Plonka
Chapter
13. Rough Sets in Economic Applications 238(34) A. Mrozek K. Skabek
Chapter
14. Multistage Rough Set Analysis of Therapeutic Experience with Acute Pancreatitis 272(23) K. Slowinski J. Stefanowski
Chapter
15. Reduction Methods for Medical Data 295(12) H. Tanaka Y. Maeda
Chapter
16. Formalization and Induction of Medical Expert System Rules Based on Rough Set Theory 307(17) S. Tsumoto
Chapter
17. Rough Sets for Database Marketing 324(12) D. Van den Poel
Chapter
18. A New Halftoning Method Based on Error Diffusion with Rough Set Filtering 336(9) H. Zeng R. Swiniarski PART 3: HYBRID APPROACHES 345(146)
Chapter
19. IRIS Revisited: A Comparison of Discriminant and Enhanced Rough Set Data Analysis 345(24) C. Browne I. Duntsch G. Gediga
Chapter
20. Applications of Rough Patterns 369(16) P. Lingras
Chapter
21. Time and Clock Information Systems: Concepts and Roughly Fuzzy Petri Net Models 385(33) J.F. Peters III
Chapter
22. The Synthesis Problem of Concurrent Systems Specified by Dynamic Information Systems 418(31) Z. Suraj
Chapter
23. Rough Sets and Artificial Neural Networks 449(22) M.S. Szczuka
Chapter
24. Genetic Algorithms in Decomposition and Classification Problems 471(20) J. Wroblewski APPENDIX 1: ROUGH SET BIBLIOGRAPHY Selected Bibliography on Rough Sets 491(64) APPENDIX 2: SOFTWARE SYSTEMS 555 GROBIAN 555(3) I. Duntsch G. Gediga RSDM: Rough sets Data Miner, A System to Add Data Mining Capabilities to RDBMS 558(4) M.C. Fernandez-Baizan E. Menasalvas Ruiz J.M. Pena B. Pardo Pastrana LERS -- A Knowledge Discovery System 562(4) J.W. Grzymala-Busse TRANCE: A Tool for Rough Data Analysis, Classification, and Clustering 566(3) W. Kowalczyk ProbRough -- A System for Probabilistic Rough Classifiers Generation 569(3) A. Lenarcik Z. Piasta The ROSETTA Software System 572(5) A. Ohrn J. Komorowski A. Skowron P. Synak RSL -- The Rough Set Library 577(4) J. Sienkiewicz Rough Family -- Software Implementation of the Rough Set Theory 581(6) R. Slowinski J. Stefanowski TAS: Tools for Analysis and Synthesis of Concurrent Processes Using Rough Set Methods 587(4) Z. Suraj RoughFuzzyLab -- A System for Data Mining and Rough and Fuzzy Sets Based Classification 591(3) R.W. Swiniarski PRIMEROSE 594(4) S. Tsumoto KDD-R: Rough Sets-Based Data Mining System 598 W. Ziarko
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