Heuristics and optimization for knowledge discovery /

Bibliographic Details
Other Authors: Sarker, Ruhul A., Abbass, Hussein A., Newton, Charles S.
Format: Printed Book
Published: Hershey : Idea Group Pub., c2002.
Subjects:
Heuristic programming.
Combinatorial optimization.
Online Access:http://www.loc.gov/catdir/toc/fy031/2001039720.html
Table of Contents:
  • Machine generated contents note: Prefacei
  • Section One: Introduction
  • Chapter 1.Introducing Data Mining and Knowledge Discovery1
  • R. Sarker, University of New South Wales, Australia
  • H. Abbass, University of New South Wales, Australia
  • C. Newton, University of New South Wales, Australia
  • Section Two: Search and Optimization
  • Chapter 2. A Heuristic Algorithm for Feature Selection Based on Optimization Techniques13
  • A.M. Bagirov, University of Ballarat, Australia
  • A.M. Rubinov, University ofBallarat, Australia
  • J. Yearwood, University ofBallarat, Australia
  • Chapter 3. Cost-Sensitive Classification using Decision Trees,Boosting and Meta Cost27
  • Kai Mai Ting, Monash University, Australia
  • Chapter 4. Heuristic Search-Based Stacking of Classifiers54
  • Agapito Ledezma, Universidad Carlos III de Madrid, Spain
  • Ricardo Aler, Universidad Carlos III de Madrid, Spain
  • Daniel Borrajo, Universidad Carlos III de Madrid, Spain
  • Chapter 5. Designing Component-Based Heuristic Search Engines for Knowledge Discovery68
  • Craig M. Howard, Lanner Group Ltd. and University of East Anglia, UK
  • Chapter 6. Clustering Mixed Incomplete Data 89
  • Jos6 Ruiz-Shulcloper, University of Tennessee, Knoxville, USA
  • & Institute of Cybernetics, Mathematics and Physics, Havana, Cuba
  • Guillermo Sanchez-Diaz, Autonomous University of the Hidalgo State, Mexico
  • Mongi A. Abidi, University of Tennessee, Knoxville, USA
  • Section Three: Statistics and Data Mining
  • Chapter 7. Bayesian Learning . 108
  • Paula Macrossan, University of New England, Australia
  • Kerrie Mengersen, University of Newcastle, Australia
  • Chapter 8. How Size Matters: The Role of Sampling in Data Mining122
  • Paul D. Scott, University of Essex, UK
  • Chapter 9. The Gamma Test142
  • Antonia J. Jones, Cardiff University, UK
  • DafyddEvans, Cardiff University, UK
  • Steve Margetts, Cardiff University, UK
  • Peter J. Durrant, Cardiff University, UK
  • Section Four: Neural Networks and Data Mining
  • Chapter 10. Neural Networks-Their Use and Abuse for Small Data Sets169
  • Denny Meyer, Massey University at Albany, New Zealand
  • Andrew Balemi, Colmar Brunton Ltd., New Zealand
  • Chris Wearing, Colmar Brunton Ltd., New Zealand
  • Chapter 11. How To Train Multilayer Perceptrons Efficiently
  • With Large Data Sets186
  • Hyeyoung Park, Brain Science Institute, Japan
  • Section Five: Applications
  • Chapter 12. Cluster Analysis of Marketing Data Examining On-line
  • Shopping Orientation: A Comparison ofk-means and Rough
  • Clustering Approaches208
  • Kevin E. Voges, Griffith University, Australia
  • Nigel K. Ll. Pope, Griffith University, Australia
  • MarkR. Brown, Griffith University, Australia
  • Chapter 13. Heuristics in Medical Data Mining226
  • Susan E. George, University of South Australia, Australia
  • Chapter 14. Understanding Credit Card User's Behaviour:
  • A Data Mining Approach241
  • A. de Carvalho, University of Guelph, Canada & University of Sio Paulo, Brazil
  • A. Braga, Federal University of Minas Gerais, Brazil
  • S. O. Rezende, University of Sao Paulo, Brazil
  • T. Ludermir, Federal University ofPemambuco, Brazil
  • E. Martineli, University of Sao Paulo, Brazil
  • Chapter 15. Heuristic Knowledge Discovery for Archaeological
  • Data Using Genetic Algorithms and Rough Sets263
  • Alina Lazar, Wayne State University, USA
  • About the Authors279
  • Index287.

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