Data Mining and Knowledge Discovery Approaches Based on Rule Induction Techniques (Massive Computing, 6)

ISBN 10: 1441941738 / ISBN 13: 9781441941732

Edité par Springer, 2011

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N° de réf. du vendeur ria9781441941732_new

Détails bibliographiques

• Titre: Data Mining and Knowledge Discovery Approaches Based on Rule Induction Techniques (Massive Computing, 6)
• Éditeur: Springer
• Date de parution: 2011
• Langue: anglais
• ISBN à 10 chiffres: 1441941738
• ISBN à 13 chiffres: 9781441941732
• Reliure: Couverture souple
• État de l'article: New

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Synopsis

2. Some Background Information 49 3. Definitions and Terminology 52 4. The One Clause at a Time (OCAT) Approach 54 4. 1 Data Binarization 54 4. 2 The One Clause at a Time (OCAT) Concept 58 4. 3 A Branch-and-Bound Approach for Inferring Clauses 59 4. 4 Inference of the Clauses for the Illustrative Example 62 4. 5 A Polynomial Time Heuristic for Inferring Clauses 65 5. A Guided Learning Approach 70 6. The Rejectability Graph of Two Collections of Examples 72 6. 1 The Definition of the Rej ectability Graph 72 6. 2 Properties of the Rejectability Graph 74 6. 3 On the Minimum Clique Cover of the Rej ectability Graph 76 7. Problem Decomposition 77 7. 1 Connected Components 77 7. 2 Clique Cover 78 8. An Example of Using the Rejectability Graph 79 9. Conclusions 82 References 83 Author's Biographical Statement 87 Chapter 3 AN INCREMENTAL LEARNING ALGORITHM FOR INFERRING LOGICAL RULES FROM EXAMPLES IN THE FRAMEWORK OF THE COMMON REASONING PROCESS, by X. Naidenova 89 1. Introduction 90 2. A Model of Rule-Based Logical Inference 96 2. 1 Rules Acquired from Experts or Rules of the First Type 97 2. 2 Structure of the Knowledge Base 98 2. 3 Reasoning Operations for Using Logical Rules of the First Type 100 2. 4 An Example of the Reasoning Process 102 3. Inductive Inference of Implicative Rules From Examples 103 3.