Using a Learning Classifier System for Clustering
Kreangsak Tamee, Larry Bull, Ouen Pinngern, Pornthep Rojanavasu, Phaitoon Srinil · 2006
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