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Knowledge Discovery in Multiple Databases

  • Book
  • © 2004

Overview

Authors:
  1. Shichao Zhang

    1. FIT, University of Technology Sydney, Australia

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  2. Chengqi Zhang

    1. FIT, University of Technology Sydney, Australia

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    You can also search for this author in PubMed   Google Scholar

  3. Xindong Wu

    1. Department of Computer Science, University of Vermont, USA

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    You can also search for this author in PubMed   Google Scholar

  • 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

Part of the book series: Advanced Information and Knowledge Processing (AI&KP)

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Table of contents (10 chapters)

  1. Front Matter

    Pages i-xii
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  2. Importance of Multi-database Mining

    • Shichao Zhang, Chengqi Zhang, Xindong Wu
    Pages 1-25

  3. Data Mining and Multi-database Mining

    • Shichao Zhang, Chengqi Zhang, Xindong Wu
    Pages 27-61

  4. Local Pattern Analysis

    • Shichao Zhang, Chengqi Zhang, Xindong Wu
    Pages 63-74

  5. Identifying Quality Knowledge

    • Shichao Zhang, Chengqi Zhang, Xindong Wu
    Pages 75-101

  6. Database Clustering

    • Shichao Zhang, Chengqi Zhang, Xindong Wu
    Pages 103-136

  7. Dealing with Inconsistency

    • Shichao Zhang, Chengqi Zhang, Xindong Wu
    Pages 137-155

  8. Identifying High-vote Patterns

    • Shichao Zhang, Chengqi Zhang, Xindong Wu
    Pages 157-184

  9. Identifying Exceptional Patterns

    • Shichao Zhang, Chengqi Zhang, Xindong Wu
    Pages 185-195

  10. Synthesizing Local Patterns by Weighting

    • Shichao Zhang, Chengqi Zhang, Xindong Wu
    Pages 197-213

  11. Conclusions and Future Work

    • Shichao Zhang, Chengqi Zhang, Xindong Wu
    Pages 215-219

  12. Back Matter

    Pages 221-233
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Keywords

About this book

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.

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)

Authors and Affiliations

  • FIT, University of Technology Sydney, Australia

    Shichao Zhang, Chengqi Zhang

  • Department of Computer Science, University of Vermont, USA

    Xindong Wu

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