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…mehr
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.Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.
Shichao Zhang, Deakin University, Geelong, VIC, Australia / Chengqi Zhang, Deakin University, Geelong, VIC, Australia / Xindong Wu, University of Vermont, Burlington, VT, USA
Inhaltsangabe
1. Importance of Multi-database Mining.- 1.1 Introduction.- 1.2 Role of Multi-database Mining in Real-world Applications.- 1.3 Multi-database Mining Problems.- 1.4 Differences Between Mono- and Multi-database Mining.- 1.5 Evolution of Multi-database Mining.- 1.6 Limitations of Previous Techniques.- 1.7 Process of Multi-database Mining.- 1.8 Features of the Defined Process.- 1.9 Major Contributions of This Book.- 1.10 Organization of the Book.- 2. Data Mining and Multi-database Mining.- 2.1 Introduction.- 2.2 Knowledge Discovery in Databases.- 2.3 Association Rule Mining.- 2.4 Research into Mining Mono-databases.- 2.5 Research into Mining Multi-databases.- 2.6 Summary.- 3. Local Pattern Analysis.- 3.1 Introduction.- 3.2 Previous Multi-database Mining Techniques.- 3.3 Local Patterns.- 3.4 Local Instance Analysis Inspired by Competition in Sports.- 3.5 The Structure of Patterns in Multi-database Environments.- 3.6 Effectiveness of Local Pattern Analysis.- 3.7 Summary.- 4. Identifying Quality Knowledge.- 4.1 Introduction.- 4.2 Problem Statement.- 4.3 Nonstandard Interpretation.- 4.4 Proof Theory.- 4.5 Adding External Knowledge.- 4.6 The Use of the Framework.- 4.7 Summary.- 5. Database Clustering.- 5.1 Introduction.- 5.2 Effectiveness of Classifying.- 5.3 Classifying Databases.- 5.4 Searching for a Good Classification.- 5.5 Algorithm Analysis.- 5.6 Evaluation of Application-independent Database Classification.- 5.7 Summary.- 6. Dealing with Inconsistency.- 6.1 Introduction.- 6.2 Problem Statement.- 6.3 Definitions of Formal Semantics.- 6.4 Weighted Majority.- 6.5 Mastering Local Pattern Sets.- 6.6 Examples of Synthesizing Local Pattern Sets.- 6.7 A Syntactic Characterization.- 6.8 Summary.- 7. Identifying High-vote Patterns.- 7.1 Introduction.- 7.2 Illustration of High-votePatterns.- 7.3 Identifying High-vote Patterns.- 7.4 Algorithm Design.- 7.5 Identifying High-vote Patterns Using a Fuzzy Logic Controller.- 7.6 High-vote Pattern Analysis.- 7.7 Suggested Patterns.- 7.8 Summary.- 8. Identifying Exceptional Patterns.- 8.1 Introduction.- 8.2 Interesting Exceptional Patterns.- 8.3 Algorithm Design.- 8.4 Identifying Exceptions with a Fuzzy Logic Controller.- 8.5 Summary.- 9. Synthesizing Local Patterns by Weighting.- 9.1 Introduction.- 9.2 Problem Statement.- 9.3 Synthesizing Rules by Weighting.- 9.4 Improvement of Synthesizing Model.- 9.5 Algorithm Analysis.- 9.6 Summary.- 10. Conclusions and Future Work.- 10.1 Conclusions.- 10.2 Future Work.- References.
1. Importance of Multi-database Mining.- 1.1 Introduction.- 1.2 Role of Multi-database Mining in Real-world Applications.- 1.3 Multi-database Mining Problems.- 1.4 Differences Between Mono- and Multi-database Mining.- 1.5 Evolution of Multi-database Mining.- 1.6 Limitations of Previous Techniques.- 1.7 Process of Multi-database Mining.- 1.8 Features of the Defined Process.- 1.9 Major Contributions of This Book.- 1.10 Organization of the Book.- 2. Data Mining and Multi-database Mining.- 2.1 Introduction.- 2.2 Knowledge Discovery in Databases.- 2.3 Association Rule Mining.- 2.4 Research into Mining Mono-databases.- 2.5 Research into Mining Multi-databases.- 2.6 Summary.- 3. Local Pattern Analysis.- 3.1 Introduction.- 3.2 Previous Multi-database Mining Techniques.- 3.3 Local Patterns.- 3.4 Local Instance Analysis Inspired by Competition in Sports.- 3.5 The Structure of Patterns in Multi-database Environments.- 3.6 Effectiveness of Local Pattern Analysis.- 3.7 Summary.- 4. Identifying Quality Knowledge.- 4.1 Introduction.- 4.2 Problem Statement.- 4.3 Nonstandard Interpretation.- 4.4 Proof Theory.- 4.5 Adding External Knowledge.- 4.6 The Use of the Framework.- 4.7 Summary.- 5. Database Clustering.- 5.1 Introduction.- 5.2 Effectiveness of Classifying.- 5.3 Classifying Databases.- 5.4 Searching for a Good Classification.- 5.5 Algorithm Analysis.- 5.6 Evaluation of Application-independent Database Classification.- 5.7 Summary.- 6. Dealing with Inconsistency.- 6.1 Introduction.- 6.2 Problem Statement.- 6.3 Definitions of Formal Semantics.- 6.4 Weighted Majority.- 6.5 Mastering Local Pattern Sets.- 6.6 Examples of Synthesizing Local Pattern Sets.- 6.7 A Syntactic Characterization.- 6.8 Summary.- 7. Identifying High-vote Patterns.- 7.1 Introduction.- 7.2 Illustration of High-votePatterns.- 7.3 Identifying High-vote Patterns.- 7.4 Algorithm Design.- 7.5 Identifying High-vote Patterns Using a Fuzzy Logic Controller.- 7.6 High-vote Pattern Analysis.- 7.7 Suggested Patterns.- 7.8 Summary.- 8. Identifying Exceptional Patterns.- 8.1 Introduction.- 8.2 Interesting Exceptional Patterns.- 8.3 Algorithm Design.- 8.4 Identifying Exceptions with a Fuzzy Logic Controller.- 8.5 Summary.- 9. Synthesizing Local Patterns by Weighting.- 9.1 Introduction.- 9.2 Problem Statement.- 9.3 Synthesizing Rules by Weighting.- 9.4 Improvement of Synthesizing Model.- 9.5 Algorithm Analysis.- 9.6 Summary.- 10. Conclusions and Future Work.- 10.1 Conclusions.- 10.2 Future Work.- References.
Rezensionen
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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