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Knowledge Discovery in Multiple Databases : Advanced Information and Knowledge Processing - Shichao Zhang

Knowledge Discovery in Multiple Databases

Advanced Information and Knowledge Processing

Hardcover Published: 30th August 2004
ISBN: 9781852337032
Number Of Pages: 233

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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.

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)

Importance of Multi-database Miningp. 1
Introductionp. 1
Role of Multi-database Mining in Real-world Applicationsp. 2
Multi-database Mining Problemsp. 4
Differences Between Mono-and Multi-database Miningp. 6
Features of Data in Multi-databasesp. 6
Features of Patterns in Multi-databasesp. 8
Evolution of Multi-database Miningp. 9
Limitations of Previous Techniquesp. 12
Process of Multi-database Miningp. 14
Description of Multi-database Miningp. 14
Practical Issues in the Processp. 16
Features of the Defined Processp. 20
Major Contributions of This Bookp. 23
Organization of the Bookp. 24
Data Mining and Multi-database Miningp. 27
Introductionp. 27
Knowledge Discovery in Databasesp. 28
Processing Steps of KDDp. 28
Data Pre-processingp. 30
Data Miningp. 31
Post Data Miningp. 33
Applications of KDDp. 34
Association Rule Miningp. 36
Research into Mining Mono-databasesp. 41
Research into Mining Multi-databasesp. 51
Parallel Data Miningp. 51
Distributed Data Miningp. 52
Application-dependent Database Selectionp. 58
Peculiarity-oriented Multi-database Miningp. 59
Summaryp. 61
Local Pattern Analysisp. 63
Introductionp. 63
Previous Multi-database Mining Techniquesp. 64
Local Patternsp. 65
Local Instance Analysis Inspired by Competition in Sportsp. 67
The Structure of Patterns in Multi-database Environmentsp. 70
Effectiveness of Local Pattern Analysisp. 73
Summaryp. 74
Identifying Quality Knowledgep. 75
Introductionp. 75
Problem Statementp. 76
Problems Faced by Traditional Multi-database Miningp. 76
Effectiveness of Identifying Quality Datap. 78
Needed Conceptsp. 80
Nonstandard Interpretationp. 82
Proof Theoryp. 88
Adding External Knowledgep. 91
The Use of the Frameworkp. 95
Applying to Real-world Applicationsp. 95
Evaluating Veridicalityp. 96
Summaryp. 100
Database Clusteringp. 103
Introductionp. 103
Effectiveness of Classifyingp. 104
Classifying Databasesp. 107
Features in Databasesp. 107
Similarity Measurementp. 108
Relevance of Databases and Classificationp. 113
Ideal Classification and Goodness Measurementp. 115
Searching for a Good Classificationp. 120
The First Step: Generating a Classificationp. 121
The Second Step: Searching for a Good Classificationp. 123
Algorithm Analysisp. 127
Procedure GreedyClassp. 127
Algorithm GoodClassp. 129
Evaluation of Application-independent Database Classificationp. 130
Dataset Selectionp. 130
Experimental Resultsp. 131
Analysisp. 134
Summaryp. 135
Dealing with Inconsistencyp. 137
Introductionp. 137
Problem Statementp. 138
Definitions of Formal Semanticsp. 139
Weighted Majorityp. 143
Mastering Local Pattern Setsp. 146
Examples of Synthesizing Local Pattern Setsp. 148
A Syntactic Characterizationp. 150
Summaryp. 155
Identifying High-vote Patternsp. 157
Introductionp. 157
Illustration of High-vote Patternsp. 158
Identifying High-vote Patternsp. 161
Algorithm Designp. 163
Searching for High-vote Patternsp. 164
Identifying High-vote Patterns: An Examplep. 165
Algorithm Analysisp. 167
Identifying High-vote Patterns Using a Fuzzy Logic Controllerp. 168
Needed Concepts in Fuzzy Logicp. 168
System Analysisp. 170
Setting Membership Functions for Input and Output Variablesp. 171
Setting Fuzzy Rulesp. 172
Fuzzificationp. 174
Inference and Rule Compositionp. 174
Defuzzificationp. 176
Algorithm Designp. 177
High-vote Pattern Analysisp. 178
Normal Distributionp. 178
The Procedure of Clusteringp. 179
Suggested Patternsp. 183
Summaryp. 183
Identifying Exceptional Patternsp. 185
Introductionp. 185
Interesting Exceptional Patternsp. 186
Measuring the Interestingnessp. 186
Behavior of Interest Measurementsp. 189
Algorithm Designp. 189
Algorithm Designp. 189
Identifying Exceptions: An Examplep. 192
Algorithm Analysisp. 193
Identifying Exceptions with a Fuzzy Logic Controllerp. 195
Summaryp. 195
Synthesizing Local Patterns by Weightingp. 197
Introductionp. 197
Problem Statementp. 198
Synthesizing Rules by Weightingp. 200
Weight of Evidencep. 200
Solving Weights of Databasesp. 201
Algorithm Designp. 205
Improvement of Synthesizing Modelp. 206
Effectiveness of Rule Selectionp. 206
Process of Rule Selectionp. 208
Optimized Algorithmp. 210
Algorithm Analysisp. 211
Procedure RuleSelectionp. 211
Algorithm RuleSynthesizingp. 212
Summaryp. 213
Conclusions and Future Workp. 215
Conclusionsp. 215
Future Workp. 218
Referencesp. 221
Subject Indexp. 231
Table of Contents provided by Publisher. All Rights Reserved.

ISBN: 9781852337032
ISBN-10: 1852337036
Series: Advanced Information and Knowledge Processing
Audience: Professional
Format: Hardcover
Language: English
Number Of Pages: 233
Published: 30th August 2004
Publisher: Springer London Ltd
Country of Publication: GB
Dimensions (cm): 23.5 x 15.5  x 1.91
Weight (kg): 1.17

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