Heuristics and optimization for knowledge discovery / [edited by] Ruhul A. Sarker, Hussein A. Abbass, Charles S. Newton
- Bib ID:
- 29084
- Format:
- Book
- Online Version:
- Table of contents
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- Description:
-
- Hershey, Pa. ; London : Idea Group Pub, c2002
- iii, 290 p. : ill. ; 26 cm.
- ISBN:
- 1930708262
- Full contents:
-
- Machine derived contents note: Preface i
- Section One: Introduction
- Chapter 1.IntroducingDataMingandKnowledge Discovery 1
- 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 Techniques 13
- 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 Cost 27
- Kai Mai Ting, Monash University, Australia
- Chapter 4. Heuristic Search-Based Stacking of Classifiers 54
- 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 Discovery 68
- 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 Mining 122
- Paul D. Scott, University of Essex, UK
- Chapter 9. The Gamma Test 142
- 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 Sets 169
- 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 Sets 186
- 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 Approaches 208
- Kevin E. Voges, Griffith University, Australia
- Nigel K. Ll. Pope, Griffith University, Australia
- MarkR. Brown, Griffith University, Australia
- Chapter 13. Heuristics in Medical Data Mining 226
- Susan E. George, University of South Australia, Australia
- Chapter 14. Understanding Credit Card User's Behaviour:
- A Data Mining Approach 241
- 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 Sets 263
- Alina Lazar, Wayne State University, USA
- About the Authors 279
- Index 287.
- Notes:
- Includes bibliographical references and index.
- Subject:
- Other authors/contributors:
- Also Titled:
- Heuristics & optimization for knowledge discovery
- Copyright:
-
In Copyright
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Copyright status was determined using the following information:
- Material type:
- Literary Dramatic Musical
- Published status:
- Published
- Publication date:
- 2002
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