Dealing with very large databases is one of the defining challenges in data mining research and development. When a data base is not a static repository of data, or if the data come from different data sources and putting all data together might amass a huge database for centralized processing, knowledge discovery in such data environments cannot be a one-time process. Existing techniques include data sampling, windowing, bagging, boosting, batch learning, hierarchical meta-learning, and parallel and distribu…
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