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Towards clustering with XCS
Author(s)
Tamee, Kreangsak
Bull, Larry
Pinngern, Ouen
Date Issued
August 27, 2007
Type
Conference Paper
Abstract
This paper presents a novel approach to clustering using an accuracy-based Learning Classifier System. Our approach achieves this by exploiting the generalization mechanisms inherent to such systems. The purpose of the work is to develop an approach to learning rules which accurately describe clusters without prior assumptions as to their number within a given dataset. Favourable comparisons to the commonly used k-means algorithm are demonstrated on a number of synthetic datasets. Copyright 2007 ACM.
Citation
Proceedings of Gecco 2007 Genetic and Evolutionary Computation Conference, 1854-1860, 2007