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.
Reihe
Auflage
Sprache
Verlagsort
Zielgruppe
Für höhere Schule und Studium
Für Beruf und Forschung
Research
Illustrationen
Maße
Höhe: 241 mm
Breite: 160 mm
Dicke: 38 mm
Gewicht
ISBN-13
978-3-7908-1120-9 (9783790811209)
DOI
10.1007/978-3-7908-1883-3
Schweitzer Klassifikation
1. Introducing the Book.- 1. Applications.- 2. Rough Approximation of a Preference Relation in a Pairwise Comparison Table.- 3. Learning Decision Rules from Similarity Based Rough Approximations.- 4. Discovery of Data Patterns with Applications to Decomposition and Classification Problems.- 5. Answering Non-Standard Queries in Distributed Knowledge-Based Systems.- 6. Approximation Spaces, Reducts and Representatives.- 7. Data Mining: A Probabilistic Rough Set Approach.- 2: Case Studies.- 8. Soft Processing of Audio Signals.- 9. A Rough Set Approach to Information Retrieval.- 10. Extraction Method Based on Rough Set Theory of Rule-Type Knowledge from Diagnostic Cases of Slope-Failure Danger Levels.- 11. Soft Computing-Based Recognition of Musical Sounds.- 12. Rough Sets in Industrial Applications.- 13. Rough Sets in Economic Applications.- 14. Multistage Rough Set Analysis of Therapeutic Experience with Acute Pancreatitis.- 15. Reduction Methods for Medical Data.- 16. Formalization and Induction of Medical Expert System Rules Based on Rough Set Theory.- 17. Rough Sets for Database Marketing.- 18. A New Halftoning Method Based on Error Diffusion with Rough Set Filtering.- 3: Hybrid Approaches.- 19. IRIS Revisited: A Comparison of Discriminant and Enhanced Rough Set Data Analysis.- 20. Applications of Rough Patterns.- 21. Time and Clock Information Systems: Concepts and Roughly Fuzzy Petri Net Models.- 22. The Synthesis Problem of Concurrent Systems Specified by Dynamic Information Systems.- 23. Rough Sets and Artificial Neural Networks.- 24. Genetic Algorithms in Decomposition and Classification Problems.- Appendix 1: Rough Set Bibliography.- Selected Bibliography on Rough Sets.- Appendix 2: Software Systems.- GROBIAN.- RSDM: Rough sets Data Miner, A System to Add Data Mining Capabilities to RDBMS.- LERS - A Knowledge Discovery System.- TRANCE: A Tool for Rough Data Analysis, Classification, and Clustering.- ProbRough - A System for Probabilistic Rough Classifiers Generation.- The ROSETTA Software System.- RSL - The Rough Set Library.- Rough Family - Software Implementation of the Rough Set Theory.- TAS: Tools for Analysis and Synthesis of Concurrent Processes Using Rough Set Methods.- RoughFuzzyLab - A System for Data Mining and Rough and Fuzzy Sets Based Classification.- PRIMEROSE.- KDD-R: Rough Sets-Based Data Mining System.
