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Mining Very Large Databases with Parallel Processing

By Alex A. Freitas and Simon H. Lavington


Mining Very Large Databases with Parallel Processing addresses the problem of large-scale data mining. It is an interdisciplinary text, describing advances in the integration of three computer science areas, namely: "intelligent" (machine learning-based) data mining techniques; relational databases and parallel processing. The basic idea is to use concepts and techniques of the latter two areas - particularly parallel processing - to speed up and scale up data mining algorithms. It is assumed that the reader has a knowledge roughly equivalent to a first degree (B.Sc.) in accurate sciences, so that (s)he is reasonably familiar with basic concepts of statistics and computer science. The primary audience of Mining Very Large Databases with Parallel Processing is industry data miners and practitioners in general, who would like to apply intelligent data mining techniques to large amounts of data. The book will also be of interest to academic researchers and post-graduate students, particularly database researchers interested in advanced, intelligent database applications and artificial intelligence researchers interested in industrial, real-world applications of machine learning

Topics: QA76
Publisher: Kluwer Academic Publishers
Year: 1998
DOI identifier: 10.1007/978-1-4615-5521-6
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