Data Mining: A Heuristic Approach: A Heuristic Approach

Front Cover
Abbass, Hussein A.
Idea Group Inc (IGI), Jul 1, 2001 - Computers - 310 pages
0 Reviews

Real life problems are known to be messy, dynamic and multi-objective, and involve high levels of uncertainty and constraints. Because traditional problem-solving methods are no longer capable of handling this level of complexity, heuristic search methods have attracted increasing attention in recent years for solving such problems. Inspired by nature, biology, statistical mechanics, physics and neuroscience, heuristics techniques are used to solve many problems where traditional methods have failed. Data Mining: A Heuristic Approach will be a repository for the applications of these techniques in the area of data mining.


What people are saying - Write a review

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


Chapter 1 From Evolution to Immune to Swarm to ? A Simple Introduction to Modern Heuristics
Chapter II Approximating Proximity for Fast and Robust DistanceBased Clustering
Evolutionary Algorithms
Chapter III On the Use of Evolutionary Algorithms in Data Mining
Chapter IV The Discovery of Interesting Nuggets Using Heuristic Techniques
Chapter V Estimation of Distribution Algorithms for Feature Subset Selection in Large Dimensionality Domains
Evolving Teams of Local Bayesian Learners
Chapter VII Evolution of Spatial Data Templates for Object Classification
Chapter IX A Building Block Approach to Genetic Programming for Rule Discovery
Chapter X An Ant Colony Algorithm for Classification Rule Discovery
Using the Immune System as Inspiration for Data Mining
An Artifical Immune Network for Data Analysis
XIII Parallel Data Mining
About the Authors

Chapter VIII Genetic Programming as a DataMining Tool

Other editions - View all

Common terms and phrases

Popular passages

Page iv - Rivero/ 1-930708-38-6 Managing Virtual Web Organizations In the 21st Century: Issues and Challenges Ulrich Franke...
Page 288 - J. Shafer. Parallel mining of association rules. IEEE Transactions on Knowledge and Data Engineering, 1996. [3] R. Agrawal and R. Srikant. Fast algorithms for mining association rules.
Page x - In addition, this book would not have been possible without the ongoing professional support from Mehdi Khosrowpour, Michele Rossi and Jan Travers at Idea Group Publishing.
Page iv - Managing Web Usage in the Workplace: A Social, Ethical and Legal Perspective, Murugan Anandarajan & Claire A.
Page iv - Multimedia Networking: Technology, Management and Applications Syed Mahbubur Rahman/ 1-930708-14-9 Cases on Worldwide E-Commerce: Theory in Action Mahesh Raisinghani/ 1-930708-27-0 Designing Instruction for Technology-Enhanced Learning Patricia L. Rogers/ 1-930708-28-9 Heuristic and Optimization for Knowledge Discovery Ruhul Amin Sarker, Hussein Aly Abbass and Charles Newton/ 1-930708-26-2 Distributed Multimedia Databases: Techniques and Applications Timothy K.
Page iv - NEW from Idea Group Publishing Data Mining: A Heuristic Approach, Hussein Aly Abbass, Ruhul Amin Sarker & Charles S.
Page iv - Neural Networks in Business: Techniques and Applications, Kate Smith and Jatinder Gupta...

References to this book

All Book Search results »

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