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Synopsis
With the large amount of data stored by many organizations, capitalists have observed that this information is an intangible asset. Unfortunately, handling large databases is a very complex process and traditional learning techniques are expensive to use. Heuristic techniques provide much help in this arena, although little is known about heuristic techniques. Heuristic and Optimization for Knowledge Discovery addresses the foundation of this topic, as well as its practical uses, and aims to fill in the gap that exists in current literature.
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About the Author
Ruhul Sarker received his Ph.D. in 1991 from DalTech, Dalhousie University, Halifax, Canada, and is currently a Senior Lecturer in Operations Research at the School of Computer Science, University of New South Wales, ADFA Campus, Canberra, Australia. Before joining at UNSW in February 1998, Dr Sarker worked with Monash University, Victoria, and the Bangladesh University of Engineering and Technology, Dhaka. His main research interests are Evolutionary Optimization, Data Mining and Applied Operations Research. He is currently involved with four edited books either as editor or co-editor, and has published more than 60 refereed papers in international journals and conference proceedings. He is also the editor of ASOR Bulletin, the national publication of the Australian Society for Operations Research.
Hussein A. Abbass gained his Ph.D. in Computer Science from the Queensland University of Technology, Brisbane, Australia. He also holds several degrees including Business, Operational Research, and Optimisation and Constraint Logic Programming, from Cairo University, Egypt, and Artificial Intelligence, from the University of Edinburgh, UK. He started his career as a Systems Administrator. In 1994, he was appointed Associate Lecturer at the Department of Computer Science, Institute of Statistical Studies and Research, Cairo University, Egypt. In 2000, he was appointed Lecturer at the School of Computer Science, University of New South Wales, ADFA Campus, Australia. His research interests include Swarm Intelligence, Evolutionary Algorithms and Heuristics where he develops approaches for the Satisfiability problem, Evolving Artificial Neural Networks, and Data Mining. He has gained experience in applying Artificial Intelligence Techniques to different areas including Budget Planning, Finance, Chemical Engineering (heat exchanger networks), Blood Management, Scheduling, and Animal Breeding and genetics.
Charles S. Newton is the Head of Computer Science, University of New South Wales (UNSW) at the Australian Defence Force Academy (ADFA) campus, Canberra. Prof. Newton is also the Deputy Rector (Education). He obtained his Ph.D. in Nuclear Physics from the Australian National University, Canberra in 1975. He joined the School of Computer Science in 1987 as a Senior Lecturer in Operations Research. In May 1993, he was appointed Head of School and became Professor of Computer Science in November 1993. Prior to joining at ADFA, Prof. Newton spent nine years in the Analytical Studies Branch of the Department of Defence. In 1989-91, Prof. Newton was the National President of the Australian Society for Operations Research. His Research Interests encompass Group Decision Support Systems, Simulation, Wargaming, Evolutionary Computation, Data Mining and Operations Research Applications. He has published extensively in national and international journals, books and conference proceedings.
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- PublisherIgi Global
- Publication date2002
- ISBN 10 1930708262
- ISBN 13 9781930708266
- BindingHardcover
- LanguageEnglish
- Number of pages296
- EditorSarker Ruhul, Abbass Hussein A., Newton Charles
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Heuristic and Optimization for Knowledge Discovery
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Heuristic and Optimization for Knowledge Discovery
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Hardcover. Condition: Good. Hardcover, [x] + 290 pages, NOT ex-library. Front blank endpaper is creased and stuck to the front board. Book is clean and bright with unmarked text, free of inscriptions and stamps. Boards show gentle handling wear. Published without a dust jacket. -- With the large amount of data stored by many organizations, capitalists have observed that this information is an intangible asset. Unfortunately, handling large databases is a very complex process and traditional learning techniques are expensive to use. Heuristic techniques provide much help in this arena, although little is known about heuristic techniques. Heuristic and Optimization for Knowledge Discovery addresses the foundation of this topic, as well as its practical uses, and aims to fill in the gap that exists in current literature. -- Contents: Section One: Introduction: - 1 Introducing Data Mining and Knowledge Discovery; Section Two: Search and Optimization: - 2 A Heuristic Algorithm for Feature Selection Based on Optimization Techniques; 3 Cost-Sensitive Classification Using Decision Trees, Boosting and MetaCost; 4 Heuristic Search-Based Stacking of Classifiers; 5 Designing Component-Based Heuristic Search Engines for Knowledge Discovery; 6 Clustering Mixed Incomplete Data; Section Three: Statistics and Data Mining: - 7 Bayesian Learning; 8 How Size Matters: The Role of Sampling in Data Mining; 9 The Gamma Test; Section Four: Neural Networks and Data Mining: - 10 Neural Networks: Their Use and Abuse for Small Data Sets; 11 How to Train Multilayer Perceptrons Efficiently with Large Data Sets; Section Five: Applications: - 12 Cluster Analysis of Marketing Data Examining On-line Shopping Orientation: A Comparison of K-means and Rough Clustering Approaches; 13 Heuristics in Medical Data Mining; 14 Understanding Credit Card User's Behaviour: A Data Mining Approach; 15 Heuristic Knowledge Discovery for Archaeological Data Using Genetic Algorithms and Rough Sets; Index. Seller Inventory # 005424
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