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Knowledge Discovery in Multiple Databases [Hardcover]

Shichao Zhang (Author), Chengqi Zhang (Author), Xindong Wu (Author)

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Book Description

Series: Advanced Information and Knowledge Processing | Publication Date: July 28, 2004

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.

#outer_postBodyPS { display: none; } #psGradient { display: none; } #psPlaceHolder { display: none; } #psExpand { display: none; } 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.

Editorial Reviews

Review

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)

Product Details

Hardcover: 248 pages
Publisher: Springer; 1st Edition. edition (July 28, 2004)
Language: English
ISBN-10: 1852337036
ISBN-13: 978-1852337032
Product Dimensions: 9.3 x 6.1 x 0.6 inches
Shipping Weight: 15.2 ounces (View shipping rates and policies)
Amazon Best Sellers Rank: #4,822,083 in Books (See Top 100 in Books)

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Inside This Book (learn more)

Key Phrases - Statistically Improbable Phrases (SIPs): (learn more)
local pattern sets, local pattern analysis, analyzing local patterns, data alining, exceptional patterns, synthesizing patterns, interstate company, peculiar data, synthesizing model, branch databases, voting ratio, database classification, tune complexity, market basket data, mining strategies, classificat ion, original raw data, ideal classification, vote patterns, base classifiers, fuzzy logic controller, association rules, logical style, relevant databases, basis coefficients

Key Phrases - Capitalized Phrases (CAPs): (learn more)
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Citations (learn more)

This book cites 6 books:

Ontological Engineering: with examples from the areas of Knowledge Management, e-Commerce and the Semantic Web. First Edition (Advanced Information and Knowledge Processing) by Asuncion Gomez-Perez in Front Matter
Advances in Distributed and Parallel Knowledge Discovery by Hillol Kargupta in Back Matter
Pacific Asia (The Making of the Contemporary World) by Yumei Zhang in Back Matter
Uncertainty Handling and Quality Assessment in Data Mining (Advanced Information and Knowledge Processing) by Michalis Vazirgiannis in Front Matter
Knowledge Asset Management by Gregoris N. Mentzas in Front Matter

See all 6 books this book cites

15 books cite this book:

Machine Learning and Data Mining for Computer Security: Methods and Applications (Advanced Information and Knowledge Processing) by Marcus A. Maloof in Front Matter
Probabilistic Modelling in Bioinformatics and Medical Informatics by Dirk Husmeier in Front Matter
Cognitive Engineering: A Distributed Approach to Machine Intelligence (Advanced Information and Knowledge Processing) by Amit Konar in Front Matter
R-Trees: Theory and Applications (Advanced Information and Knowledge Processing) by Yannis Manolopoulos in Front Matter
Evolutionary Multiobjective Optimization: Theoretical Advances and Applications (Advanced Information and Knowledge Processing) by Ajith Abraham in Front Matter

See all 15 books citing this book

Books on Related Topics (learn more)

Encyclopedia of Data Warehousing and Mining by John Wang

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Knowledge Discovery in Databases by Jean-Francois Boulicaut

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Principles of Data Mining and Knowledge Discovery by Jan Zytkow

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