Call for Chapters and Contributions
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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






Hussein Aly Abbass Amein        
Lecturer in Computer Science,           Email: abbass@cs.adfa.edu.au    
Australian Defence Force Academy,       http: http://www.cs.adfa.edu.au/~abbass
School of Computer Science,             Tel.(M) (+61) 0402212977
University College,                     Tel.(H) (+61) (2) 62578757
University of New Wouth Wales,          Tel.(W) (+61) (2) 62688158              
Canberra, ACT2600, Australia.           Fax.(W) (+61) (2) 62688581