Towards clustering with XCS

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. Categories and Subject Descriptors I.2.8 [Artificial Intelligence]: Problem Solving, Control Methods and Search – backtracking, control theory, dynamic programming, graph and tree search strategies, heuristic methods, plan execution formation and execution, scheduling. General Terms Algorithms, Performance, Experimentation. Keywords Data mining, k-means, Learning Classifier Systems.

• Type: Conference
• Year: 2007
• Where: GECCO
• Authors: Kreangsak Tamee, Larry Bull, Ouen Pinngern
• Keywords: Accuracy-based Learning Classifier, GECCO 2007, Learning Classifier Systems, Plan Execution Formation