The Springer International Engineering and Computer Science

By: Robert Hilderman, Howard J. Hamilton

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Hardcover | 30 September 2001

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188 Pages

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23.5 x 15.88 x 1.91

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Knowledge Discovery and Measures of Interest is a reference book for knowledge discovery researchers, practitioners, and students. The knowledge discovery researcher will find that the material provides a theoretical foundation for measures of interest in data mining applications where diversity measures are used to rank summaries generated from databases. The knowledge discovery practitioner will find solid empirical evidence on which to base decisions regarding the choice of measures in data mining applications. The knowledge discovery student in a senior undergraduate or graduate course in databases and data mining will find the book is a good introduction to the concepts and techniques of measures of interest.
In Knowledge Discovery and Measures of Interest, we study two closely related steps in any knowledge discovery system: the generation of discovered knowledge; and the interpretation and evaluation of discovered knowledge. In the generation step, we study data summarization, where a single dataset can be generalized in many different ways and to many different levels of granularity according to domain generalization graphs. In the interpretation and evaluation step, we study diversity measures as heuristics for ranking the interestingness of the summaries generated.
The objective of this work is to introduce and evaluate a technique for ranking the interestingness of discovered patterns in data. It consists of four primary goals:

To introduce domain generalization graphs for describing and guiding the generation of summaries from databases.
To introduce and evaluate serial and parallel algorithms that traverse the domain generalization space described by the domain generalization graphs.
To introduce and evaluate diversity measures as heuristic measures of interestingness for ranking summaries generated from databases.
To develop the preliminary foundation for a theory of interestingness within the context of ranking summaries generated from databases.

Knowledge Discovery and Measures of Interest is suitable as a secondary text in a graduate level course and as a reference for researchers and practitioners in industry.

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You Can Find This Book In

Non-Fiction Computing & I.T.Databases Data Mining Computer Science Artificial Intelligence Expert Systems & Knowledge-Based Systems Medicine Medicine in General Image Processing Computer Programming & Software Development Algorithms & Data Structures

Data Capture & Analysis Mathematics Mathematical Theory of Computation

List of Figures	p. ix
List of Tables	p. xi
Preface	p. xv
Acknowledgments	p. xix
Introduction	p. 1
KDD in a Nutshell	p. 1
The Mining Step	p. 2
The Interpretation and Evaluation Step	p. 7
Objective of the Book	p. 9
Background and Related Work	p. 11
Data Mining Techniques	p. 11
Classification	p. 11
Association	p. 12
Clustering	p. 13
Correlation	p. 14
Other Techniques	p. 15
Interestingness Measures	p. 15
Rule Interest Function	p. 15
J-Measure	p. 16
Itemset Measures	p. 16
Rule Templates	p. 17
Projected Savings	p. 17
I-Measures	p. 18
Silbershatz and Tuzhilin's Interestingness	p. 18
Kamber and Shinghal's Interestingness	p. 19
Credibility	p. 20
General Impressions	p. 20
Distance Metric	p. 21
Surprisingness	p. 21
Gray and Orlowska's Interestingness	p. 22
Dong and Li's Interestingness	p. 22
Reliable Exceptions	p. 23
Peculiarity	p. 23
A Data Mining Technique	p. 25
Definitions	p. 25
The Serial Algorithm	p. 26
General Overview	p. 26
Detailed Walkthrough	p. 28
The Parallel Algorithm	p. 30
General Overview	p. 31
Detailed Walkthrough	p. 32
Complexity Analysis	p. 33
Attribute-Oriented Generalization	p. 33
The All_Gen Algorithm	p. 33
A Comparison with Commercial OLAP Systems	p. 34
Heuristic Measures of Interestingness	p. 37
Diversity	p. 37
Notation	p. 39
The Sixteen Diversity Measures	p. 39
The I[subscript Variance] Measure	p. 39
The I[subscript Simpson] Measure	p. 40
The I[subscript Shannon] Measure	p. 40
The I[subscript Total] Measure	p. 41
The I[subscript Max] Measure	p. 41
The I[subscript McIntosh] Measure	p. 42
The I[subscript Lorenz] Measure	p. 42
The I[subscript Gini] Measure	p. 43
The I[subscript Berger] Measure	p. 44
The I[subscript Schutz] Measure	p. 44
The I[subscript Bray] Measure	p. 44
The I[subscript Whittaker] Measure	p. 44
The I[subscript Kullback] Measure	p. 45
The I[subscript MacArthur] Measure	p. 45
The I[subscript Theil] Measure	p. 46
The I[subscript Atkinson] Measure	p. 46
An Interestingness Framework	p. 47
Interestingness Principles	p. 47
Summary	p. 49
Theorems and Proofs	p. 51
Minimum Value Principle	p. 51
Maximum Value Principle	p. 63
Skewness Principle	p. 79
Permutation Invariance Principle	p. 84
Transfer Principle	p. 84
Experimental Analyses	p. 99
Evaluation of the All_Gen Algorithm	p. 99
Serial vs Parallel Performance	p. 100
Speedup and Efficiency Improvements	p. 103
Evaluation of the Sixteen Diversity Measures	p. 104
Comparison of Assigned Ranks	p. 105
Analysis of Ranking Similarities	p. 107
Analysis of Summary Complexity	p. 112
Distribution of Index Values	p. 117
Conclusion	p. 123
Summary	p. 123
Areas for Future Research	p. 125
Appendices	p. 141
Comparison of Assigned Ranks	p. 141
Ranking Similarities	p. 149
Summary Complexity	p. 155
Index	p. 161
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The Springer International Engineering and Computer Science

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