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Data Mining: A Heuristic Approach
http://www.cs.adfa.edu.au/~abbass/Book/DMHA.html
Editors: H.A. Abbass, R. Sarkar, and C. Newton
Publisher: Idea Group Publishing, USA
This book volume will be a repository for the applications of heuristic techniques in data mining. With roots in optimisation, artificial intelligence, and statistics, data mining is an interdisciplinary area that is concerned with finding patterns in databases. These patterns might be the expected trend of the fashion in women's clothes, the potential change in the prices of some shares in the stock exchange market, the prospective behaviour of some competitors, or the causes of a budding virus. With the large amount of data stored in many organizations, businessmen observed that these data are an important intangible asset, if not the most important one, in their organizations. This instantiated an enormous amount of research, searching for learning methods that are capable of recognising novel and non-trivial patterns in databases. Unfortunately, handling large databases is a very complex process and traditional learning techniques such as Neural Networks and traditional Decision Trees are expensive to use. New optimisation techniques such as support vector machines and kernels methods, as well as statistical techniques such as Bayesian learning, are widely used in the field of data mining nowadays.
However, these techniques are computationally expensive. Obviously, heuristic techniques provide much help in this arena. Notwithstanding, there are few books in the area of heuristics and few more in the area of data mining. Surprisingly, no single book has been published to put together these two fast-changing inter-related fields.
Topics
The use of heuristics (Evolutionary algorithms, simulated annealing, tabu search, swarm intelligence, biological agents, memetic, and others) in the following areas
Feature selection.
Data cleaning.
Clustering, classification, prediction, and association rules.
Optimisation methods for data mining.
Kernels and support vector machines.
Fast algorithms for training neural networks.
Bayesian inference and learning.
Survey chapters are also welcomed.
and other related topics
Important dates
Abstract submission: August 15, 2000
Acceptance of abstract: September 15, 2000
Full chapter due: January 15, 2001
Notification of full-chapter acceptance: March 1, 2001
Final Version Due: April 30, 2001
Estimated publication date: Fall 2001 by Idea Group Publishing
Contact information:
Send electronic submissions to one of the editors at
abbass@cs.adfa.edu.au
ruhul@cs.adfa.edu.au
csn@cs.adfa.edu.au
Hard copies should be sent to any of the editors at:
School of Computer Science, University College,
University of New South Wales,
Australian Defence Force Academy,
Canberra, ACT2600, Australia.
Fax submission to:
02-62688581 within Australia
+61-2-62688581 International