Rough Set Theory and Granular Computing

ISBN: 978-3-642-05614-7 (Print) 978-3-540-36473-3 (Online)

Table of contents (28 chapters)

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  1. Front Matter

    Pages I-XV

  2. Bayes’ Theorem — the Rough Set Perspective

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      Pages 1-12

      Bayes’ Theorem — the Rough Set Perspective

  3. Approximation Spaces in Rough Neurocomputing

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      Pages 13-22

      Approximation Spaces in Rough Neurocomputing

  4. Soft Computing Pattern Recognition: Principles, Integrations and Data Mining

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      Pages 23-34

      Soft Computing Pattern Recognition: Principles, Integrations and Data Mining

  5. Generalizations and New Theories

    1. Front Matter

      Pages 35-35

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      Pages 37-46

      Generalization of Rough Sets Using Weak Fuzzy Similarity Relations

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      Pages 47-57

      Two Directions toward Generalization of Rough Sets

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      Pages 59-68

      Two Generalizations of Multisets

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      Pages 69-78

      Interval Probability and Its Properties

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      Pages 79-87

      On Fractal Dimension in Information Systems

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      Pages 89-96

      A Remark on Granular Reasoning and Filtration

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      Pages 97-108

      Towards Discovery of Relevant Patterns from Parameterized Schemes of Information Granule Construction

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      Pages 109-121

      Approximate Markov Boundaries and Bayesian Networks: Rough Set Approach

  6. Data Mining and Rough Sets

    1. Front Matter

      Pages 123-123

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      Pages 125-135

      Mining High Order Decision Rules

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      Pages 137-145

      Association Rules from a Point of View of Conditional Logic

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      Pages 147-156

      Association Rules with Additional Semantics Modeled by Binary Relations

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      Pages 157-166

      A Knowledge-Oriented Clustering Method Based on Indiscernibility Degree of Objects

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      Pages 167-176

      Some Effective Procedures for Data Dependencies in Information Systems

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      Pages 177-185

      Improving Rules Induced from Data Describing Self-Injurious Behaviors by Changing Truncation Cutoff and Strength

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      Pages 187-196

      The Variable Precision Rough Set Inductive Logic Programming Model and Future Test Cases in Web Usage Mining

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      Pages 197-207

      Rough Set and Genetic Programming

  7. Conflict Analysis and Data Analysis

    1. Front Matter

      Pages 209-209

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      Book Chapter

      Pages 211-221

      Rough Set Approach to Conflict Analysis

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