Knowledge Discovery in Multiple Databases
By: by Shichao Zhang, Chengqi Zhang, Xindong Wu
Contributors: Zhang, Chengqi; Wu, Xindong
Series: Advanced Information and Knowledge Processing, 1610-3947; Advanced information and knowledge processing
Publisher: Springer London; Imprint; Springer
Published: 2004
Place of publication: London
Language: eng
Format: [electronic resource] /
Number of pages: 233
ISBN-13: 9780857293886
Description: 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. <STRONG>Knowledge Discovery in Multiple Databases</STRONG> 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. <STRONG>Knowledge Discovery in Multiple Databases</STRONG> 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.