Rough Sets and Data Mining

I Expositions.- 1 Rough Sets.- 2 Data Mining: Trends in Research and Development.- 3 A Review of Rough Set Models.- 4 Rough Control: A Perspective.- II Applications.- 5 Machine Learning & Knowledge Acquisition, Rough Sets, and The English Semantic Code.- 6 Generation of Multiple Knowledge From Databases Basedon Rough Set Theory.- 7 Fuzzy Controllers:An Integrated Approach Based on Fuzzy Logic, Rough Sets, and Evolutionary Computing.- 8 Rough Real Functions and Rough Controllers.- 9 A Fusion of Rough Sets, Modified Rough Sets, and Genetic Algorithms For Hybrid Diagnostic Systems.- 10 Rough Sets As A Tool For Studying Attribute Dependencies in The Urinary Stones Treatment Data Set.- III Related Areas.- 11 Data Mining Using Attribute- Oriented Generalization and Information Reduction.- 12 Neighborhoods, Rough Sets, and Query Relaxation in Cooperative Answering.- 13 Resolving Queries Through Cooperation in Multi-Agent Systems.- 14 Synthesis of Decision Systems From Data Tables.- 15 Combination Of Rough and Fuzzy Sets Based on Alpha-Level Sets.- 16 Theories That Combine Many Equivalence and Subset Relations.- IV Generalization.- 17 Generalized Rough Sets in Contextual Spaces.- 18 Maintenance Of Reducts in The Variable Precision Rough Set Model.- 19 Probabilistic Rough Classifiers With Mixture Of Discrete and Continuous Attributes.- 20 Algebraic Formulation of Machine Learning Methods Based on Rough Sets, Matroid Theory, and Combinatorial Geometry.- 21 Topological Rough Algebras.