# Mining Very Large Databases with Parallel Processing

Part of the The Kluwer International Series on Advances in Database Systems book series (ADBS, volume 9)

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The book is divided into three parts. The first part presents a comprehensive review of intelligent data mining techniques such as rule induction, instance-based learning, neural networks and genetic algorithms. Likewise, the second part presents a comprehensive review of parallel processing and parallel databases. Each of these parts includes an overview of commercially-available, state-of-the-art tools. The third part deals with the application of parallel processing to data mining. The emphasis is on finding generic, cost-effective solutions for realistic data volumes. Two parallel computational environments are discussed, the first excluding the use of commercial-strength DBMS, and the second using parallel DBMS servers.

It is assumed that the reader has a knowledge roughly equivalent to a first degree (BSc) in accurate sciences, so that (s)he is reasonably familiar with basic concepts of statistics and computer science.

The primary audience for

DBMS algorithms artificial intelligence computer science data mining database database systems genetic algorithms neural network neural networks parallel processing relational database

- DOI https://doi.org/10.1007/978-1-4615-5521-6
- Copyright Information Kluwer Academic Publishers 2000
- Publisher Name Springer, Boston, MA
- eBook Packages Springer Book Archive
- Print ISBN 978-1-4613-7523-4
- Online ISBN 978-1-4615-5521-6
- Series Print ISSN 1386-2944
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