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.