The Web has emerged as a large distributed data repository and information onthe Internet and in existing transaction databases can be analyzed forcommercial ga... » 

The Web has emerged as a large distributed data repository and information onthe Internet and in existing transaction databases can be analyzed forcommercial gains in decision making. Therefore how to efficiently identifyquality 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 introductionto the latest advancements in multidatabase mining and presents a localpattern analysis framework for pattern discovery from multiple data sources.Based on this framework data preparation techniques in multiple databases anapplicationindependent database classification for data reduction andefficient algorithms for pattern discovery from multiple databases aredescribed. Knowledge Discovery in Multiple Databases is suitable forresearchers professionals and students in data mining distributed dataanalysis and machine learning who are interested in multidatabase mining. Itis also appropriate for use as a text supplement for broader courses that mightinvolve knowledge discovery in databases and data mining. TOCFrom thecontentsImportance of MultiDatabase Mining. Data Mining and MultiDatabaseMining. Local Pattern Analysis. Identifying Quality Knowledge. DatabaseClustering. Dealing with Inconsistency. Identifying HighVote Patterns.Identifying Exceptional Patterns. Synthesizing Global Patterns from LocalPatterns by Weighting. Conclusions and Future Work. «