Mining Very Large Databases with Parallel Processing

Freitas, Alex A. and Lavington, Simon H. (1998) Mining Very Large Databases with Parallel Processing. Kluwer Academic Publishers, Boston, 228 pp. ISBN 0-7923-8048-7 & 978-0792380481 . (The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided)

The full text of this publication is not available from this repository. (Contact us about this Publication)


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.

Item Type: Book
Subjects: Q Science > QA Mathematics (inc Computing science) > QA 76 Software, computer programming,
Divisions: Faculties > Science Technology and Medical Studies > School of Computing > Applied and Interdisciplinary Informatics Group
Depositing User: Mark Wheadon
Date Deposited: 21 Aug 2009 22:15
Last Modified: 11 Jul 2014 13:32
Resource URI: (The current URI for this page, for reference purposes)
  • Depositors only (login required):