Del i serien Advanced Information and Knowledge Processing

Knowledge Discovery in Multiple Databases

Inbunden, Engelska, 2004

AvShichao Zhang,Chengqi Zhang,Xindong Wu

1 459 kr

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The Web has emerged as a large, distributed data repository, & information on the Internet & 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. Knowledge Discovery in Multiple Databases provides a comprehensive introduction to the latest advancements in multi-database mining, & 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, & efficient algorithms for pattern discovery from multiple databases are described. This book is suitable for researchers, professionals & students in data mining, distributed data analysis, and machine learning. It is also appropriate for use as a text supplement for broader courses involving knowledge discovery in databases & data mining.

Produktinformation

Utgivningsdatum2004-08-30
Mått155 x 235 x 20 mm
Vikt528 g
FormatInbunden
SpråkEngelska
SerieAdvanced Information and Knowledge Processing
Antal sidor233
Upplaga2004
FörlagSpringer London Ltd
ISBN9781852337032

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Databaser inom Data och IT

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.

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)