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More About This Textbook
Overview
Rough Sets and Data Mining: Analysis of Imprecise Data is an edited collection of research chapters on the most recent developments in rough set theory and data mining. The chapters in this work cover a range of topics that focus on discovering dependencies among data, and reasoning about vague, uncertain and imprecise information. The authors of these chapters have been careful to include fundamental research with explanations as well as coverage of rough set tools that can be used for mining data bases.
The contributing authors consist of some of the leading scholars in the fields of rough sets, data mining, machine learning and other areas of artificial intelligence. Among the list of contributors are Z. Pawlak, J Grzymala-Busse, K. Slowinski, and others.
Rough Sets and Data Mining: Analysis of Imprecise Data will be a useful reference work for rough set researchers, data base designers and developers, and for researchers new to the areas of data mining and rough sets.
Editorial Reviews
Booknews
A collection of 21 papers summarizing recent developments in fundamental research and practical tools for discovering dependencies among data, and reasoning about vague, uncertain, and imprecise information. They discuss such aspects as the generation of multiple knowledge from databases based on rough set theory, data mining using attribute-oriented generalization and information reduction, maintaining reducts in the variable precision rough set model, topological rough algebras, theories combining many equivalence and subset relations, and resolving queries through cooperation in multi- agent systems. Some of the papers have been expanded and updated from a 1995 computer science conference. Annotation c. by Book News, Inc., Portland, Or.Product Details
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Table of Contents
Preface. Part I: Expositions. 1. Rough Sets; Z. Pawlak. 2. Data Mining: Trends in Research and Development; J. Deogun, et al. 3. A Review of Rough Set Models; Y.Y. Yao, et al. 4. Rough Control: A Perspective; T. Munakata. Part II: Applications. 5. Machine Learning & Knowledge Acquisition, Rough Sets, and the English Semantic Code; J. Grzymala-Busse, et al. 6. Generation of Multiple Knowledge from Databases Based on Rough Set Theory; X. Hu, et al. 7. Fuzzy Controllers: An Integrated Approach Based on Fuzzy Logic, Rough Sets, and Evolutionary Computing; T.Y. Lin. 8. Rough Real Functions and Rough Controllers; Z. Pawlak. 9. A Fusion of Rough Sets, Modified Rough Sets, and Genetic Algorithms for Hybrid Diagnostic Systems; R. Hashemi, et al. 10. Rough Sets as a Tool for Studying Attribute Dependencies in the Urinary Stones Treatment Data Set; J. Stefanowski, K. Slowinski. Part III: Related Areas. 11. Data Mining Using Attribute-Oriented Generalization and Information Reduction; N. Cercone, et al. 12. Neighborhoods, Rough Sets, and Query Relaxation in Cooperative Answering; J.B. Michael, T.Y. Lin. 13. Resolving Queries Through Cooperation in Multi-Agent Systems; Z. Ras. 14. Synthesis of Decision Systems From Data Tables; A. Skowron, L. Polkowski. 15. Combination of Rough and Fuzzy Sets Based on Alpha-Level Sets; Y.Y. Yao. 16. Theories that Combine Many Equivalence and Subset Relations; J. Zytkow, R. Zembowicz. Part IV: Generalization. 17. Generalized Rough Sets in Contextual Spaces; E. Bryniarski, U. Wybraniec- Skardowksa. 18. Maintenance of Reducts in the Variable Precision Rough Set Model; M. Kryszkiewicz. 19. Probabilistic Rough Classifiers with Mixture of Discrete and Continuous Attributes; A. Lenarcik, Z. Piasta. 20. Algebraic Formulation of Machine Learning Methods Based on Rough Sets, Matroid Theory, and Combinatorial Geometry; S. Tsumoto, H. Tanaka. 21. Topological Rough Algebras; A. Wasilewska. Index.