A learning cLassifier system approach to clustering

Tamee, Kreangsak; Bull, Larry; Pinngern, Ouen

doi:10.1109/ISDA.2006.62

A learning cLassifier system approach to clustering

Author(s)

Tamee, Kreangsak

Bull, Larry

Pinngern, Ouen

Date Issued

December 1, 2006

Type

Conference Paper

DOI

10.1109/ISDA.2006.62

Abstract

This paper presents a novel approach to clustering using a simple accuracy-based Learning Classifier System. Our approach achieves this by exploiting the evolutionary computing and reinforcement learning techniques 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 datasets. © 2006 IEEE.

Citation

Proceedings Isda 2006 Sixth International Conference on Intelligent Systems Design and Applications, 1, 621-626, 2006