Knowledge discovery and measures of interest

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Knowledge discovery and measures of interest / by Robert J. Hilderman, Howard J. Hamilton

資料種別:

図書

出版情報:

Boston, MA : Kluwer Academic Publishers, c2001

形態:

xvii, 162 p. ; 25 cm

シリーズ名:

The Kluwer international series in engineering and computer science ; SECS 638 <BA0004729X>

著者名:

ISBN:

9780792375074 [0792375076]

書誌ID:

BA54408405

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 and index

主題:

Data mining; Database searching; Expert systems (Computer science)

言語:

英語

目次情報:

List of Figures

List of Tables

Preface

Acknowledgments

Introduction / 1.：

KDD in a Nutshell / 1.1：

The Mining Step / 1.1.1：

The Interpretation and Evaluation Step / 1.1.2：

Objective of the Book / 1.2：

Background and Related Work / 2.：

Data Mining Techniques / 2.1：

Classification / 2.1.1：

Association / 2.1.2：

Clustering / 2.1.3：

Correlation / 2.1.4：

Other Techniques / 2.1.5：

Interestingness Measures / 2.2：

Rule Interest Function / 2.2.1：

J-Measure / 2.2.2：

Itemset Measures / 2.2.3：

Rule Templates / 2.2.4：

Projected Savings / 2.2.5：

I-Measures / 2.2.6：

Silbershatz and Tuzhilin's Interestingness / 2.2.7：

Kamber and Shinghal's Interestingness / 2.2.8：

Credibility / 2.2.9：

General Impressions / 2.2.10：

Distance Metric / 2.2.11：

Surprisingness / 2.2.12：

Gray and Orlowska's Interestingness / 2.2.13：

Dong and Li's Interestingness / 2.2.14：

Reliable Exceptions / 2.2.15：

Peculiarity / 2.2.16：

A Data Mining Technique / 3.：

Definitions / 3.1：

The Serial Algorithm / 3.2：

General Overview / 3.2.1：

Detailed Walkthrough / 3.2.2：

The Parallel Algorithm / 3.3：

Complexity Analysis / 3.3.1：

Attribute-Oriented Generalization / 3.4.1：

The All_Gen Algorithm / 3.4.2：

A Comparison with Commercial OLAP Systems / 3.5：

Heuristic Measures of Interestingness / 4.：

Diversity / 4.1：

Notation / 4.2：

The Sixteen Diversity Measures / 4.3：

The I[subscript Variance] Measure / 4.3.1：

The I[subscript Simpson] Measure / 4.3.2：

The I[subscript Shannon] Measure / 4.3.3：

The I[subscript Total] Measure / 4.3.4：

The I[subscript Max] Measure / 4.3.5：

The I[subscript McIntosh] Measure / 4.3.6：

The I[subscript Lorenz] Measure / 4.3.7：

The I[subscript Gini] Measure / 4.3.8：

The I[subscript Berger] Measure / 4.3.9：

The I[subscript Schutz] Measure / 4.3.10：

The I[subscript Bray] Measure / 4.3.11：

The I[subscript Whittaker] Measure / 4.3.12：

The I[subscript Kullback] Measure / 4.3.13：

The I[subscript MacArthur] Measure / 4.3.14：

The I[subscript Theil] Measure / 4.3.15：

The I[subscript Atkinson] Measure / 4.3.16：

An Interestingness Framework / 5.：

Interestingness Principles / 5.1：

Summary / 5.2：

Theorems and Proofs / 5.3：

Minimum Value Principle / 5.3.1：

Maximum Value Principle / 5.3.2：

Skewness Principle / 5.3.3：

Permutation Invariance Principle / 5.3.4：

Transfer Principle / 5.3.5：

Experimental Analyses / 6.：

Evaluation of the All_Gen Algorithm / 6.1：

Serial vs Parallel Performance / 6.1.1：

Speedup and Efficiency Improvements / 6.1.2：

Evaluation of the Sixteen Diversity Measures / 6.2：

Comparison of Assigned Ranks / 6.2.1：

Analysis of Ranking Similarities / 6.2.2：

Analysis of Summary Complexity / 6.2.3：

Distribution of Index Values / 6.2.4：

Conclusion / 7.：

Areas for Future Research / 7.1：

Appendices

Ranking Similarities

Summary Complexity

Index

List of Figures

List of Tables

Preface

Acknowledgments

Introduction / 1.：

KDD in a Nutshell / 1.1：

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