Heuristic and Optimization for Knowledge Discovery

Front Cover
Abbass, Hussein A.
Idea Group Inc (IGI), Jul 1, 2001 - Computers - 296 pages
0 Reviews

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.


What people are saying - Write a review

We haven't found any reviews in the usual places.


Chapter I Introducing Data Mining and Knowledge Discover
Section TWO Search and Optimization
Chapter II A Heuristic Algorithm for Feature Selection Based on Optimization Techniques
Chapter III CostSensitive Classification Using Decision Trees Boosting and MetaCost
Chapter IV Heuristic SearchBased Stacking of Classifiers
Chapter V Designing ComponentBased Heuristic Search Engines for Knowledge Discovery
Chapter VI Clustering Mixed Incomplete Data
Section Three Statistics and Data Mining
Section Four Neural Networks and Data Mining
Chapter X Neural NetworksTheir Use and Abuse for Small Data Sets
Chapter XI How to Train Multilayer Perceptrons Efficiently With Large Data Sets
Section Five Applications
A Comparison of kmeans and Rough Clustering Approaches
Chapter XIII Heuristics in Medical Data Mining
A Data Mining Approach
Chapter XV Heuristic Knowledge Discovery for Acheaeological Data Using Genetic Algorithms and Rough Sets

Chapter VII Bayesian Learning
The Role of Sampling in Data Mining
Chapter IX The Gamma Test
About the Authors

Other editions - View all

Common terms and phrases

Popular passages

Page x - of all involved in the collation and the review process of the book, without whose support the project could not have been satisfactorily completed. Most of the authors of chapters included in this
Page x - Thanks go to all those who provided constructive and comprehensive reviews and comments. A further special note of thanks goes to all the staff at Idea Group Publishing, whose contributions throughout the whole process from inception to
Page x - In closing, we wish to thank all the authors for their insight and excellent contributions to this book. In addition, this book would not have been possible without the ongoing professional support from Senior Editor Dr. Mehdi Khosrowpour, Managing Editor Ms. Jan Travers and Development Editor Ms. Michele Rossi at Idea Group Publishing. Finally, we want to thank our families for their love

References to this book

About the author (2001)

Hussein A. Abbass is the director of the Artificial Life and Adaptive Robotics Laboratory at the School of Information Technology and Electrical Engineering at the Australian Defense Force Academy campus of the University of New South Wales. Dr. Abbass is a Senior Member of the IEEE and has more than 15 years experience in industry and academia and more than a hundred fully refereed papers in international journals and conferences. He teaches computational intelligence related subjects and his research focuses on multi-agent systems, data mining, and artificial life models with applications to defence, security and business.

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.

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. [Editor]

Bibliographic information