Focusing Solutions for Data Mining

Analytical Studies and Experimental Results in Real-World Domains

By: Thomas Reinartz

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Paperback | 18 August 1999

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In the first part, this book analyzes the knowledge discovery process in order to understand the relations between knowledge discovery steps and focusing. The part devoted to the development of focusing solutions opens with an analysis of the state of the art, then introduces the relevant techniques, and finally culminates in implementing a unified approach as a generic sampling algorithm, which is then integrated into a commercial data mining system. The last part evaluates specific focusing solutions in various application domains. The book provides various appendicies enhancing easy accessibility.
The book presents a comprehensive introduction to focusing in the context of data mining and knowledge discovery. It is written for researchers and advanced students, as well as for professionals applying data mining and knowledge discovery techniques in practice.

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Introduction	p. 1
Knowledge Discovery in Databases and Data Mining	p. 1
Focusing for Data Mining	p. 4
Overview	p. 6
Knowledge Discovery in Databases	p. 11
Knowledge Discovery Process	p. 11
Humans in the Loop	p. 11
KDD Project Phases	p. 13
Data Preparation	p. 16
From Business Data to Data Mining Input	p. 17
Data Selection and Focusing	p. 19
Data Mining Goals	p. 20
From Understanding to Predictive Modeling	p. 21
Classification	p. 23
Data Characteristics: Notations and Definitions	p. 25
Database Tables	p. 25
Statistical Values	p. 29
Data Mining Algorithms	p. 31
Classification Algorithms	p. 32
Top Down Induction of Decision Trees	p. 33
Nearest Neighbor Classifiers	p. 37
Selecting the Focusing Context	p. 44
Focusing Tasks	p. 45
Focusing Concepts: An Overview	p. 45
Focusing Specification	p. 47
Focusing Input	p. 48
Focusing Output	p. 49
Focusing Criterion	p. 50
Focusing Context	p. 52
Data Characteristics	p. 54
Data Mining Algorithms	p. 55
Focusing Success	p. 55
Filter Evaluation	p. 57
Wrapper Evaluation	p. 64
Evaluation Criteria	p. 70
Selecting the Focusing Task	p. 83
Focusing Solutions	p. 85
State of the Art: A Unifying View	p. 85
The Unifying Framework of Existing Focusing Solutions	p. 85
Sampling	p. 87
Clustering	p. 95
Prototyping	p. 104
More Intelligent Sampling Techniques	p. 109
Existing Reusable Components	p. 111
Advanced Leader Sampling	p. 113
Similarity-Driven Sampling	p. 134
A Unified Approach to Focusing Solutions	p. 149
Generic Sampling	p. 150
Generic Sampling in a Commercial Data Mining System	p. 153
Analytical Studies	p. 159
An Average Case Analysis	p. 159
Experimental Validation of Theoretical Claims	p. 170
Experimental Results	p. 173
Experimental Design	p. 173
Experimental Procedure	p. 173
Data Characteristics	p. 179
Results and Evaluation	p. 182
Filter Evaluation	p. 182
Wrapper Evaluation for C4.5	p. 188
Wrapper Evaluation for IB	p. 195
Comparing Filter and Wrapper Evaluation for C4.5	p. 201
Comparing Filter and Wrapper Evaluation for IB	p. 208
Comparing Wrapper Evaluation for C4.5 and IB	p. 215
Focusing Advice	p. 222
Sorting, Stratification, and Prototype Weighting	p. 222
Focusing Solutions in Focusing Contexts	p. 223
Conclusions	p. 231
Summary and Contributions	p. 231
More Related Work	p. 235
Future Work	p. 236
Closing Remarks	p. 238
Bibliography	p. 239
Acknowledgments	p. 253
Notations	p. 257
Indices, Variables, and Functions	p. 257
Algorithms and Procedures	p. 264
More Evaluation Criteria	p. 267
Filter Evaluation Criteria	p. 267
Wrapper Evaluation Criteria	p. 272
Remaining Proofs	p. 277
Generic Sampling in GenSam	p. 281
More Experimental Results	p. 283
Index	p. 303
Curriculum Vitae	p. 309
Table of Contents provided by Publisher. All Rights Reserved.

Focusing Solutions for Data Mining

Analytical Studies and Experimental Results in Real-World Domains

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