Vol. 1: Data Mining: A Heuristic Approach
Hussein A. Abbass, Ruhul A. Sarker, and Charles S. Newton
Vol. 2: Heuristics and Optimisation for Knowledge Discovery
Ruhul A. Sarker, Hussein A. Abbass, and Charles S. Newton
Real life problems are known to be messy, dynamic, and multi-objective, and involve high level of uncertainty and constraints. Traditional optimisation / problem-solving methods are no longer capable of handling this level of complexity. Heuristic search methods have attracted increasing attentions in recent years for solving complex optimisation problems. Nowadays, heuristic techniques went beyond their traditional definition as a simple rule of thumb. They are inspired by nature, biology, statistical mechanics, physics, and neuroscience, to name but a few. Heuristic techniques, such as evolutionary and genetic algorithms, simulated annealing, and Tabu search, proofed themselves in solving many problems where traditional problem solving methods failed. Modern heuristic techniques, such as Ant Colonies, Immune, Memetic, and scatter search, are taking firm steps as robust problem solving mechanisms. This book volume will be a repository for the applications of heuristic techniques in data mining, an important area nowadays.
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 inference and learning, are attracting most of the attentions in the field of data mining nowadays. 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.
The use of heuristics in the following areas
| Feature selection. | Data cleaning. | Clustering, classification, prediction, and association. | Optimisation methods for data mining. | Kernels and support vector machines. | Fast algorithms for training neural networks. | Bayesian inference and learning methods. | Survey chapters are also welcomed. |
| 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, USA |
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