Towards clustering with learning classifier systems

Tamee, Kreangsak; Bull, Larry; Pinngern, Ouen

doi:10.1007/978-3-540-78979-6_9

Towards clustering with learning classifier systems

Author(s)

Tamee, Kreangsak

Bull, Larry

Pinngern, Ouen

Date Issued

July 16, 2008

Type

Article

DOI

10.1007/978-3-540-78979-6_9

Abstract

This chapter 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. © 2008 Springer-Verlag Berlin Heidelberg.

Citation

Studies in Computational Intelligence, 125, 191-204, 2008