The only book that covers the emerging topic of multiple database mining Focuses on developing new techniques for multi-database mining Urgent need to analyse data in multi-databases as a great deal of multi-databases are widely used in organisations Includes supplementary material: sn.pub/extras

Klappentext

The Web has emerged as a large, distributed data repository, and information on the Internet and in existing transaction databases can be analyzed for commercial gains in decision making. Therefore, how to efficiently identify quality knowledge from different data sources uncovers a significant challenge. This challenge has attracted wide interest from both academia and the industry. Knowledge Discovery in Multiple Databases provides a comprehensive introduction to the latest advancements in multi-database mining, and presents a local-pattern analysis framework for pattern discovery from multiple data sources. Based on this framework, data preparation techniques in multiple databases, an application-independent database classification for data reduction, and efficient algorithms for pattern discovery from multiple databases are described. Knowledge Discovery in Multiple Databases is suitable for researchers, professionals and students in data mining, distributed data analysis, and machine learning, who are interested in multi-database mining. It is also appropriate for use as a text supplement for broader courses that might involve knowledge discovery in databases and data mining.

Inhalt

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.