Abbass H.A., Sarker R.A., Newton C.S. (Eds.) Data Mining: A Heuristic Approach

Idea Group Publishing, 2002. — 300 p. — ISBN: 1930708254.This book is a repository of research papers describing the applications of modern heuristics to data mining. This is a unique–and as far as we know, the first–book that provides up-to-date research in coupling these two topics of modern heuristics and data mining. Although it is by all means an incomplete coverage, it does provide some leading research in this area.General Heuristics
Hussein A. Abbass
From Evolution to Immune to Swarm to …? A Simple Introduction to Modern Heuristics
Vladimir Estivill-Castro, Michael Houle
Approximating Proximity for Fast and Robust Distance-Based Clustering
Evolutionary Algorithms
Erick Cantú-Paz, Chandrika Kamath
On the Use of Evolutionary Algorithms in Data Mining
Beatriz de la Iglesia, Victor J. Rayward-Smith
The discovery of interesting nuggets using heuristic techniques
Iñaki Inza, Pedro Larrañaga, Basilio Sierra
Estimation of Distribution Algorithms for Feature Subset Selection in Large Dimensionality Domains
Jorge Muruzábal
Towards the Cross-Fertilization of Multiple Heuristics: Evolving Teams of Local Bayesian Learners
Neil Dunstan, Michael de Raadt
Evolution of Spatial Data Templates for Object Classification
Genetic Programming
Peter W.H. Smith
Genetic Programming as a Data-Mining Tool
A.P. Engelbrecht, Sonja Rouwhorst, L. Schoeman
A Building Block Approach to Genetic Programming for Rule Discovery
Rafael S. Parpinelli, Heitor S. Lopes, Alex A. Freitas
Ant Colony Optimization and Immune Systems
An Ant Colony Algorithm for Classification Rule Discovery
Jon Timmis, Thomas Knight
Artificial Immune Systems: Using the Immune System as Inspiration for Data Mining
Leandro Nunes de Castro, Fernando J. Von Zuben
aiNet: An Artificial Immune Network for Data Analysis
Parallel Data Mining
David Taniar, J. Wenny Rahayu
Parallel Data Mining