Heuristics and optimization for knowledge discovery [electronic resource] /
Heuristics & optimization for knowledge discovery [electronic resource]
[edited by] Ruhul A. Sarker, Hussein A. Abbass, Charles S. Newton.
- Hershey : Idea Group Pub., c2002.
- 1 online resource.
Includes bibliographical references and index.
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.