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This book outlines the core theory and practice of data mining and knowledge discovery (DM & KD) examining theoretical foundations for various methods, and presenting an array of examples, many drawn from real-life applications. Most theoretical developments are accompanied by extensive empirical analysis, offering a deep insight into both theoretical and practical aspects of the subject. The book presents the combined research experiences of 40 expert contributors of world renown.
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Dalla quarta di copertina
This book will give the reader a perspective into the core theory and practice of data mining and knowledge discovery (DM&KD). Its chapters combine many theoretical foundations for various DM&KD methods, and they present a rich array of examples many of which are drawn from real-life applications. Most of the theoretical developments discussed are accompanied by an extensive empirical analysis, which should give the reader both a deep theoretical and practical insight into the subjects covered.
The book presents the combined research experiences of its 40 authors gathered during a long search in gleaning new knowledge from data. The last page of each chapter has a brief biographical statement of its contributors, who are world-renowned experts.
Audience
The intended audience for this book includes graduate students studying data mining who have some background in mathematical logic and discrete optimization, as well as researchers and practitioners in the same area.
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Data Mining and Knowledge Discovery Approaches Based on Rule Induction Techniques
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Data Mining and Knowledge Discovery Approaches Based on Rule Induction Techniques (Massive Computing, 6)
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Data Mining and Knowledge Discovery Approaches Based on Rule Induction Techniques (Massive Computing, 6)
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Data Mining and Knowledge Discovery Approaches Based on Rule Induction Techniques (Hardcover)
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Springer-Verlag New York Inc., New York, NY, 2006
ISBN 10: 038734294X
ISBN 13: 9780387342948
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Hardcover. Condizione: new. Hardcover. 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. 2 The One Clause at a Time (OCAT) Concept 58 4. 2 Structure of the Knowledge Base 98 2. 3 Reasoning Operations for Using Logical Rules of the First Type 100 2. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Codice articolo 9780387342948
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Data Mining and Knowledge Discovery Approaches Based on Rule Induction Techniques (Massive Computing, 6)
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Data Mining and Knowledge Discovery Approaches Based on Rule Induction Techniques
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Gebunden. Condizione: New. Provides a unique perspective into the core of data mining and knowledge discovery (DM and KD), combining many theoretical foundations for the behavior and capabilities of various DM and KD methods This book outlines the core theory and practi. Codice articolo 5910110
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Data Mining and Knowledge Discovery Approaches Based on Rule Induction Techniques
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Data Mining and Knowledge Discovery Approaches Based on Rule Induction Techniques
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Data Mining and Knowledge Discovery Approaches Based on Rule Induction Techniques
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Data Mining and Knowledge Discovery Approaches Based on Rule Induction Techniques
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Buch. Condizione: Neu. Neuware - 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. Codice articolo 9780387342948
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