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.
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 B! udget Planning, Finance, Chemical Engineering (heat exchanger networks), Blood Management, Scheduling, and Animal Breeding and genetics.
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.
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.
