Knowledge Discovery in Multiple Databases

By: Shichao Zhang, Chengqi Zhang, Xindong Wu

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Hardcover | 30 August 2004

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Hardcover
250 Pages

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24.13 x 15.88 x 1.91

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Many organizations have an urgent need of mining their multiple databases inherently distributed in branches (distributed data). In particular, as the Web is rapidly becoming an information flood, individuals and organizations can take into account low-cost information and knowledge on the Internet when making decisions. How to efficiently identify quality knowledge from different data sources has become a significant challenge. This challenge has attracted a great many researchers including the au thors who have developed a local pattern analysis, a new strategy for dis covering some kinds of potentially useful patterns that cannot be mined in traditional multi-database mining techniques. Local pattern analysis deliv ers high-performance pattern discovery from multiple databases. There has been considerable progress made on multi-database mining in such areas as hierarchical meta-learning, collective mining, database classification, and pe culiarity discovery. While these techniques continue to be future topics of interest concerning multi-database mining, this book focuses on these inter esting issues under the framework of local pattern analysis. The book is intended for researchers and students in data mining, dis tributed data analysis, machine learning, and anyone else who is interested in multi-database mining. It is also appropriate for use as a text supplement for broader courses that might also involve knowledge discovery in databases and data mining.

Industry Reviews

From the reviews: "The book contains the latest on research in database multi-mining (32 papers published after 2000) and offers for consideration a local-pattern analysis framework for pattern discovery from multiple data sources. Starting from the local pattern in multiple data bases, the authors propose ! a new pattern named 'high-vote' pattern based on statistical analysis of vote ratio received by a pattern from each branch of the company." (Silviu Craciunas, Zentralblatt MATH, Vol. 1067, 2005)

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Non-Fiction Computing & I.T.Databases Computer Science Computer Programming & Software Development Algorithms & Data Structures

Importance of Multi-database Mining	p. 1
Introduction	p. 1
Role of Multi-database Mining in Real-world Applications	p. 2
Multi-database Mining Problems	p. 4
Differences Between Mono-and Multi-database Mining	p. 6
Features of Data in Multi-databases	p. 6
Features of Patterns in Multi-databases	p. 8
Evolution of Multi-database Mining	p. 9
Limitations of Previous Techniques	p. 12
Process of Multi-database Mining	p. 14
Description of Multi-database Mining	p. 14
Practical Issues in the Process	p. 16
Features of the Defined Process	p. 20
Major Contributions of This Book	p. 23
Organization of the Book	p. 24
Data Mining and Multi-database Mining	p. 27
Introduction	p. 27
Knowledge Discovery in Databases	p. 28
Processing Steps of KDD	p. 28
Data Pre-processing	p. 30
Data Mining	p. 31
Post Data Mining	p. 33
Applications of KDD	p. 34
Association Rule Mining	p. 36
Research into Mining Mono-databases	p. 41
Research into Mining Multi-databases	p. 51
Parallel Data Mining	p. 51
Distributed Data Mining	p. 52
Application-dependent Database Selection	p. 58
Peculiarity-oriented Multi-database Mining	p. 59
Summary	p. 61
Local Pattern Analysis	p. 63
Introduction	p. 63
Previous Multi-database Mining Techniques	p. 64
Local Patterns	p. 65
Local Instance Analysis Inspired by Competition in Sports	p. 67
The Structure of Patterns in Multi-database Environments	p. 70
Effectiveness of Local Pattern Analysis	p. 73
Summary	p. 74
Identifying Quality Knowledge	p. 75
Introduction	p. 75
Problem Statement	p. 76
Problems Faced by Traditional Multi-database Mining	p. 76
Effectiveness of Identifying Quality Data	p. 78
Needed Concepts	p. 80
Nonstandard Interpretation	p. 82
Proof Theory	p. 88
Adding External Knowledge	p. 91
The Use of the Framework	p. 95
Applying to Real-world Applications	p. 95
Evaluating Veridicality	p. 96
Summary	p. 100
Database Clustering	p. 103
Introduction	p. 103
Effectiveness of Classifying	p. 104
Classifying Databases	p. 107
Features in Databases	p. 107
Similarity Measurement	p. 108
Relevance of Databases and Classification	p. 113
Ideal Classification and Goodness Measurement	p. 115
Searching for a Good Classification	p. 120
The First Step: Generating a Classification	p. 121
The Second Step: Searching for a Good Classification	p. 123
Algorithm Analysis	p. 127
Procedure GreedyClass	p. 127
Algorithm GoodClass	p. 129
Evaluation of Application-independent Database Classification	p. 130
Dataset Selection	p. 130
Experimental Results	p. 131
Analysis	p. 134
Summary	p. 135
Dealing with Inconsistency	p. 137
Introduction	p. 137
Problem Statement	p. 138
Definitions of Formal Semantics	p. 139
Weighted Majority	p. 143
Mastering Local Pattern Sets	p. 146
Examples of Synthesizing Local Pattern Sets	p. 148
A Syntactic Characterization	p. 150
Summary	p. 155
Identifying High-vote Patterns	p. 157
Introduction	p. 157
Illustration of High-vote Patterns	p. 158
Identifying High-vote Patterns	p. 161
Algorithm Design	p. 163
Searching for High-vote Patterns	p. 164
Identifying High-vote Patterns: An Example	p. 165
Algorithm Analysis	p. 167
Identifying High-vote Patterns Using a Fuzzy Logic Controller	p. 168
Needed Concepts in Fuzzy Logic	p. 168
System Analysis	p. 170
Setting Membership Functions for Input and Output Variables	p. 171
Setting Fuzzy Rules	p. 172
Fuzzification	p. 174
Inference and Rule Composition	p. 174
Defuzzification	p. 176
Algorithm Design	p. 177
High-vote Pattern Analysis	p. 178
Normal Distribution	p. 178
The Procedure of Clustering	p. 179
Suggested Patterns	p. 183
Summary	p. 183
Identifying Exceptional Patterns	p. 185
Introduction	p. 185
Interesting Exceptional Patterns	p. 186
Measuring the Interestingness	p. 186
Behavior of Interest Measurements	p. 189
Algorithm Design	p. 189
Algorithm Design	p. 189
Identifying Exceptions: An Example	p. 192
Algorithm Analysis	p. 193
Identifying Exceptions with a Fuzzy Logic Controller	p. 195
Summary	p. 195
Synthesizing Local Patterns by Weighting	p. 197
Introduction	p. 197
Problem Statement	p. 198
Synthesizing Rules by Weighting	p. 200
Weight of Evidence	p. 200
Solving Weights of Databases	p. 201
Algorithm Design	p. 205
Improvement of Synthesizing Model	p. 206
Effectiveness of Rule Selection	p. 206
Process of Rule Selection	p. 208
Optimized Algorithm	p. 210
Algorithm Analysis	p. 211
Procedure RuleSelection	p. 211
Algorithm RuleSynthesizing	p. 212
Summary	p. 213
Conclusions and Future Work	p. 215
Conclusions	p. 215
Future Work	p. 218
References	p. 221
Subject Index	p. 231
Table of Contents provided by Publisher. All Rights Reserved.

Knowledge Discovery in Multiple Databases

At a Glance

Hardcover

Industry Reviews

Shipping

How to return your order

Defective items

You Can Find This Book In

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