Skip to main content
SpringerLink
Log in
Book cover

Data Mining Using Grammar Based Genetic Programming and Applications

  • Book
  • © 2002

Overview

Authors:
  1. Man Leung Wong

    1. Lingnan University, Hong Kong

    View author publications

    You can also search for this author in PubMed   Google Scholar

  2. Kwong Sak Leung

    1. The Chinese University of Hong Kong, Hong Kong

    View author publications

    You can also search for this author in PubMed   Google Scholar

Part of the book series: Genetic Programming (GPEM, volume 3)

This is a preview of subscription content, log in via an institution to check access.

Access this book

Log in via an institution
eBook USD 129.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book USD 169.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Other ways to access

Licence this eBook for your library

Institutional subscriptions

Table of contents (9 chapters)

  1. Front Matter

    Pages i-xiv
    Download chapter PDF

  2. Introduction

    Pages 1-8

  3. An Overview of Data Mining

    Pages 9-25

  4. An Overview on Evolutionary Algorithms

    Pages 27-55

  5. Inductive Logic Programming

    Pages 57-69

  6. The Logic Grammars Based Genetic Programming System (LOGENPRO)

    Pages 71-100

  7. Data Mining Applications Using LOGENPRO

    Pages 101-136

  8. Applying LOGENPRO for Rule Learning

    Pages 137-160

  9. Medical Data Mining

    Pages 161-167

  10. Conclusion and Future Work

    Pages 169-175

  11. Back Matter

    Pages 177-213
    Download chapter PDF
Back to top

Keywords

About this book

Data mining involves the non-trivial extraction of implicit, previously unknown, and potentially useful information from databases. Genetic Programming (GP) and Inductive Logic Programming (ILP) are two of the approaches for data mining. This book first sets the necessary backgrounds for the reader, including an overview of data mining, evolutionary algorithms and inductive logic programming. It then describes a framework, called GGP (Generic Genetic Programming), that integrates GP and ILP based on a formalism of logic grammars. The formalism is powerful enough to represent context- sensitive information and domain-dependent knowledge. This knowledge can be used to accelerate the learning speed and/or improve the quality of the knowledge induced.
A grammar-based genetic programming system called LOGENPRO (The LOGic grammar based GENetic PROgramming system) is detailed and tested on many problems in data mining. It is found that LOGENPRO outperforms some ILP systems. We have also illustrated how to apply LOGENPRO to emulate Automatically Defined Functions (ADFs) to discover problem representation primitives automatically. By employing various knowledge about the problem being solved, LOGENPRO can find a solution much faster than ADFs and the computation required by LOGENPRO is much smaller than that of ADFs. Moreover, LOGENPRO can emulate the effects of Strongly Type Genetic Programming and ADFs simultaneously and effortlessly.
Data Mining Using Grammar Based Genetic Programming and Applications is appropriate for researchers, practitioners and clinicians interested in genetic programming, data mining, and the extraction of data from databases.

Authors and Affiliations

  • Lingnan University, Hong Kong

    Man Leung Wong

  • The Chinese University of Hong Kong, Hong Kong

    Kwong Sak Leung

Bibliographic Information

  • Book Title: Data Mining Using Grammar Based Genetic Programming and Applications

  • Authors: Man Leung Wong, Kwong Sak Leung

  • Series Title: Genetic Programming

  • DOI: https://doi.org/10.1007/b116131

  • Publisher: Springer New York, NY

  • eBook Packages: Springer Book Archive

  • Copyright Information: Springer Science+Business Media New York 2002

  • Hardcover ISBN: 978-0-7923-7746-7Published: 29 February 2000

  • Softcover ISBN: 978-1-4757-8421-3Published: 21 March 2013

  • eBook ISBN: 978-0-306-47012-7Published: 02 December 2005

  • Series ISSN: 1566-7863

  • Edition Number: 1

  • Number of Pages: XIV, 214

  • Topics: Artificial Intelligence, Data Structures and Information Theory

Publish with us

Policies and ethics

Back to top

Search

Navigation