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Mining Very Large Databases with Parallel Processing

  • Alex A. Freitas
  • Simon H. Lavington

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

Table of contents

  1. Front Matter
    Pages i-xiii
  2. Introduction

    1. Alex A. Freitas, Simon H. Lavington
      Pages 1-4
  3. Knowledge Discovery and Data Mining

    1. Front Matter
      Pages 5-5
    2. Alex A. Freitas, Simon H. Lavington
      Pages 7-17
    3. Alex A. Freitas, Simon H. Lavington
      Pages 19-29
    4. Alex A. Freitas, Simon H. Lavington
      Pages 31-40
    5. Alex A. Freitas, Simon H. Lavington
      Pages 41-50
    6. Alex A. Freitas, Simon H. Lavington
      Pages 51-57
  4. Parallel Database Systems

    1. Front Matter
      Pages 59-59
    2. Alex A. Freitas, Simon H. Lavington
      Pages 61-69
    3. Alex A. Freitas, Simon H. Lavington
      Pages 71-78
    4. Alex A. Freitas, Simon H. Lavington
      Pages 79-86
  5. Parallel Data Mining

    1. Front Matter
      Pages 87-87
    2. Alex A. Freitas, Simon H. Lavington
      Pages 89-108
    3. Alex A. Freitas, Simon H. Lavington
      Pages 109-142
    4. Alex A. Freitas, Simon H. Lavington
      Pages 143-172
    5. Alex A. Freitas, Simon H. Lavington
      Pages 173-179
  6. Back Matter
    Pages 181-208

About this book

Introduction

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.
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 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 postgraduate students, particularly database researchers, interested in advanced, intelligent database applications, and artificial intelligence researchers interested in industrial, real-world applications of machine learning.

Keywords

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

Authors and affiliations

  • Alex A. Freitas
    • 1
  • Simon H. Lavington
    • 1
  1. 1.University of EssexColchesterUK

Bibliographic information

  • 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
  • Buy this book on publisher's site