Graphical models : methods for data analysis and mining

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Graphical models : methods for data analysis and mining / Christian Borgelt and Rudolf Kruse

資料種別:

図書

出版情報:

Chichester : J. Wiley, c2002

形態:

viii, 358 p. ; 24 cm

著者名:

ISBN:

9780470843376 [0470843373]

書誌ID:

BA55975025

子書誌情報

フルテキスト

所蔵情報

他の版・巻

書誌詳細

注記:

Includes bibliographical references (p. 329-347) and index

主題:

Data mining; Mathematical statistics--Graphic methods

言語:

英語

目次情報:

Preface

Introduction / 1：

Data and Knowledge / 1.1：

Knowledge Discovery and Data Mining / 1.2：

The KDD Process / 1.2.1：

Data Mining Tasks / 1.2.2：

Data Mining Methods / 1.2.3：

Graphical Models / 1.3：

Outline of this Book / 1.4：

Imprecision and Uncertainty / 2：

Modeling Inferences / 2.1：

Imprecision and Relational Algebra / 2.2：

Uncertainty and Probability Theory / 2.3：

Possibility Theory and the Context Model / 2.4：

Experiments with Dice / 2.4.1：

The Context Model / 2.4.2：

The Insufficient Reason Principle / 2.4.3：

Overlapping Contexts / 2.4.4：

Mathematical Formalization / 2.4.5：

Normalization and Consistency / 2.4.6：

Possibility Measures / 2.4.7：

Mass Assignment Theory / 2.4.8：

Degrees of Possibility for Decision Making / 2.4.9：

Conditional Degrees of Possibility / 2.4.10：

Open Problems / 2.4.11：

Decomposition / 3：

Decomposition and Reasoning / 3.1：

Relational Decomposition / 3.2：

A Simple Example / 3.2.1：

Reasoning in the Simple Example / 3.2.2：

Decomposability of Relations / 3.2.3：

Tuple-Based Formalization / 3.2.4：

Possibility-Based Formalization / 3.2.5：

Conditional Possibility and Independence / 3.2.6：

Probabilistic Decomposition / 3.3：

Factorization of Probability Distributions / 3.3.1：

Conditional Probability and Independence / 3.3.4：

Possibilistic Decomposition / 3.4：

Transfer from Relational Decomposition / 3.4.1：

Conditional Degrees of Possibility and Independence / 3.4.2：

Possibility versus Probability / 3.5：

Graphical Representation / 4：

Conditional Independence Graphs / 4.1：

Axioms of Conditional Independence / 4.1.1：

Graph Terminology / 4.1.2：

Separation in Graphs / 4.1.3：

Dependence and Independence Maps / 4.1.4：

Markov Properties of Graphs / 4.1.5：

Graphs and Decompositions / 4.1.6：

Markov Networks and Bayesian Networks / 4.1.7：

Evidence Propagation in Graphs / 4.2：

Propagation in Polytrees / 4.2.1：

Join Tree Propagation / 4.2.2：

Other Evidence Propagation Methods / 4.2.3：

Computing Projections / 5：

Databases of Sample Cases / 5.1：

Relational and Sum Projections / 5.2：

Expectation Maximization / 5.3：

Maximum Projections / 5.4：

Computation via the Support / 5.4.1：

Computation via the Closure / 5.4.3：

Experimental Results / 5.4.4：

Limitations / 5.4.5：

Naive Classifiers / 6：

Naive Bayes Classifiers / 6.1：

The Basic Formula / 6.1.1：

Relation to Bayesian Networks / 6.1.2：

A Naive Possibilistic Classifier / 6.1.3：

Classifier Simplification / 6.3：

Learning Global Structure / 6.4：

Principles of Learning Global Structure / 7.1：

Learning Relational Networks / 7.1.1：

Learning Probabilistic Networks / 7.1.2：

Learning Possibilistic Networks / 7.1.3：

Components of a Learning Algorithm / 7.1.4：

Evaluation Measures / 7.2：

General Considerations / 7.2.1：

Notation and Presuppositions / 7.2.2：

Relational Evaluation Measures / 7.2.3：

Probabilistic Evaluation Measures / 7.2.4：

Possibilistic Evaluation Measures / 7.2.5：

Search Methods / 7.3：

Exhaustive Graph Search / 7.3.1：

Guided Random Graph Search / 7.3.2：

Conditional Independence Search / 7.3.3：

Greedy Search / 7.3.4：

Learning Local Structure / 7.4：

Local Network Structure / 8.1：

Inductive Causation / 8.2：

Correlation and Causation / 9.1：

Causal and Probabilistic Structure / 9.2：

Stability and Latent Variables / 9.3：

The Inductive Causation Algorithm / 9.4：

Critique of the Underlying Assumptions / 9.5：

Evaluation / 9.6：

Applications / 10：

Application in Telecommunications / 10.1：

Application at Volkswagen / 10.2：

Application at Daimler Chrysler / 10.3：

Proofs of Theorems / A：

Proof of Theorem 4.1.2 / A.1：

Proof of Theorem 4.1.18 / A.2：

Proof of Theorem 4.1.20 / A.3：

Proof of Theorem 4.1.22 / A.4：

Proof of Theorem 4.1.24 / A.5：

Proof of Theorem 4.1.26 / A.6：

Proof of Theorem 4.1.27 / A.7：

Proof of Theorem 5.4.8 / A.8：

Proof of Theorem 7.3.2 / A.9：

Proof of Lemma 7.2.2 / A.10：

Proof of Lemma 7.2.4 / A.11：

Proof of Lemma 7.2.6 / A.12：

Proof of Theorem 7.3.4 / A.13：

Proof of Theorem 7.3.5 / A.14：

Proof of Theorem 7.3.6 / A.15：

Proof of Theorem 7.3.8 / A.16：

Software Tools / B：

Bibliography

Index

Preface

Introduction / 1：

Data and Knowledge / 1.1：

Knowledge Discovery and Data Mining / 1.2：

The KDD Process / 1.2.1：

Data Mining Tasks / 1.2.2：

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