Data Mining Knowledge Discovery by Triantaphyllou Evangelos (38 results)

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Taschenbuch. Condition: Neu. Data Mining and Knowledge Discovery via Logic-Based Methods | Theory, Algorithms, and Applications | Evangelos Triantaphyllou | Taschenbuch | xxxiv | Englisch | 2012 | Springer | EAN 9781461426134 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.

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Buch. Condition: Neu. Neuware -The importance of having ef cient and effective methods for data mining and kn- ledge discovery (DM&KD), to which the present book is devoted, grows every day and numerous such methods have been developed in recent decades. There exists a great variety of different settings for the main problem studied by data mining and knowledge discovery, and it seems that a very popular one is formulated in terms of binary attributes. In this setting, states of nature of the application area under consideration are described by Boolean vectors de ned on some attributes. That is, by data points de ned in the Boolean space of the attributes. It is postulated that there exists a partition of this space into two classes, which should be inferred as patterns on the attributes when only several data points are known, the so-called positive and negative training examples. The main problem in DM&KD is de ned as nding rules for recognizing (cl- sifying) new data points of unknown class, i. e. , deciding which of them are positive and which are negative. In other words, to infer the binary value of one more attribute, called the goal or class attribute. To solve this problem, some methods have been suggested which construct a Boolean function separating the two given sets of positive and negative training data points.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 388 pp. Englisch.

Taschenbuch. Condition: Neu. Neuware -The importance of having ef cient and effective methods for data mining and kn- ledge discovery (DM&KD), to which the present book is devoted, grows every day and numerous such methods have been developed in recent decades. There exists a great variety of different settings for the main problem studied by data mining and knowledge discovery, and it seems that a very popular one is formulated in terms of binary attributes. In this setting, states of nature of the application area under consideration are described by Boolean vectors de ned on some attributes. That is, by data points de ned in the Boolean space of the attributes. It is postulated that there exists a partition of this space into two classes, which should be inferred as patterns on the attributes when only several data points are known, the so-called positive and negative training examples. The main problem in DM&KD is de ned as nding rules for recognizing (cl- sifying) new data points of unknown class, i. e. , deciding which of them are positive and which are negative. In other words, to infer the binary value of one more attribute, called the goal or class attribute. To solve this problem, some methods have been suggested which construct a Boolean function separating the two given sets of positive and negative training data points.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 384 pp. Englisch.

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Buch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - The importance of having ef cient and effective methods for data mining and kn- ledge discovery (DM&KD), to which the present book is devoted, grows every day and numerous such methods have been developed in recent decades. There exists a great variety of different settings for the main problem studied by data mining and knowledge discovery, and it seems that a very popular one is formulated in terms of binary attributes. In this setting, states of nature of the application area under consideration are described by Boolean vectors de ned on some attributes. That is, by data points de ned in the Boolean space of the attributes. It is postulated that there exists a partition of this space into two classes, which should be inferred as patterns on the attributes when only several data points are known, the so-called positive and negative training examples. The main problem in DM&KD is de ned as nding rules for recognizing (cl- sifying) new data points of unknown class, i. e. , deciding which of them are positive and which are negative. In other words, to infer the binary value of one more attribute, called the goal or class attribute. To solve this problem, some methods have been suggested which construct a Boolean function separating the two given sets of positive and negative training data points.

Taschenbuch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - The importance of having ef cient and effective methods for data mining and kn- ledge discovery (DM&KD), to which the present book is devoted, grows every day and numerous such methods have been developed in recent decades. There exists a great variety of different settings for the main problem studied by data mining and knowledge discovery, and it seems that a very popular one is formulated in terms of binary attributes. In this setting, states of nature of the application area under consideration are described by Boolean vectors de ned on some attributes. That is, by data points de ned in the Boolean space of the attributes. It is postulated that there exists a partition of this space into two classes, which should be inferred as patterns on the attributes when only several data points are known, the so-called positive and negative training examples. The main problem in DM&KD is de ned as nding rules for recognizing (cl- sifying) new data points of unknown class, i. e. , deciding which of them are positive and which are negative. In other words, to infer the binary value of one more attribute, called the goal or class attribute. To solve this problem, some methods have been suggested which construct a Boolean function separating the two given sets of positive and negative training data points.

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Taschenbuch. Condition: Neu. Data Mining and Knowledge Discovery Approaches Based on Rule Induction Techniques | Giovanni Felici (u. a.) | Taschenbuch | xlviii | Englisch | 2011 | Springer US | EAN 9781441941732 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.

Gebunden. Condition: 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.

Taschenbuch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - 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.

Paperback. Condition: Brand New. 748 pages. 9.00x6.00x1.80 inches. In Stock.

Buch. Condition: 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.

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Condition: new. Questo è un articolo print on demand.