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Knowledge Discovery in Multiple Databases

By (author) Shichao Zhang, By (author) Chengqi Zhang, By (author) Xindong Wu

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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 auÂ thors who have developed a local pattern analysis, a new strategy for disÂ covering some kinds of potentially useful patterns that cannot be mined in traditional multi-database mining techniques. Local pattern analysis delivÂ 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 peÂ culiarity discovery. While these techniques continue to be future topics of interest concerning multi-database mining, this book focuses on these interÂ esting issues under the framework of local pattern analysis. The book is intended for researchers and students in data mining, disÂ 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.

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