Rough Set Theory and Granular Computing

Bayes' Theorem - the Rough Set Perspective.- 1 Introduction.- 2 Bayes' Theorem.- 3 Information Systems and Approximation of Sets.- 4 Decision Language.- 5 Decision Algorithms.- 6 Decision Rules in Information Systems.- 7 Properties of Decision Rules.- 8 Decision Tables and Flow Graphs.- 9 Illustrative Example.- 10 Conclusion.- References.- Approximation Spaces in Rough Neurocomputing.- 1 Introduction.- 2 Approximation Spaces in Rough Set Theory.- 3 Generalizations of Approximation Spaces.- 4 Information Granule Systems and Approximation Spaces.- 5 Classifiers as Information Granules.- 6 Approximation Spaces for Information Granules.- 7 Approximation Spaces in Rough-Neuro Computing.- 8 Conclusion.- References.- Soft Computing Pattern Recognition: Principles, Integrations and Data Mining.- 1 Introduction.- 2 Relevance of Fuzzy Set Theory in Pattern Recognition.- 3 Relevance of Neural Network Approaches.- 4 Genetic Algorithms for Pattern Recognition.- 5 Integration and Hybrid Systems.- 6 Evolutionary Rough Fuzzy MLP.- 7 Data mining and knowledge discovery.- References.- I. Generalizations and New Theories.- Generalization of Rough Sets Using Weak Fuzzy Similarity Relations.- Two Directions toward Generalization of Rough Sets.- Two Generalizations of Multisets.- Interval Probability and Its Properties.- On Fractal Dimension in Information Systems.- A Remark on Granular Reasoning and Filtration.- Towards Discovery of Relevant Patterns from Parameterized Schemes of Information Granule Construction.- Approximate Markov Boundaries and Bayesian Networks: Rough Set Approach.- II. Data Mining and Rough Sets.- Mining High Order Decision Rules.- Association Rules from a Point of View of Conditional Logic.- Association Rules with Additional Semantics Modeled by BinaryRelations.- A Knowledge-Oriented Clustering Method Based on Indiscernibility Degree of Objects.- Some Effective Procedures for Data Dependencies in Information Systems.- Improving Rules Induced from Data Describing