A novel multi-objective genetic algorithm for clustering

Authors:
Oliver Kirkland;Victor J. Rayward-Smith;Beatriz de la Iglesia
Affiliations:
School of Computing Sciences, University of East Anglia, Norwich, UK;School of Computing Sciences, University of East Anglia, Norwich, UK;School of Computing Sciences, University of East Anglia, Norwich, UK
Venue:
IDEAL'11 Proceedings of the 12th international conference on Intelligent data engineering and automated learning
Year:
2011

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Abstract

In this paper, we introduce a new Multi-Objective Clustering algorithm (MOCA). The use of Multi-Objective optimisation in clustering is desirable because it permits the incorporation of different criteria for cluster quality. Since the criteria to establish what constitutes a good clustering is far from clear, it is beneficial to develop algorithms that allow for multiple criteria to be accommodated. The algorithm proposes a new implementation of multi-objective clustering by using a centroid based technique. We explain the implementation details and perform experimental work to establish its worth. We construct a robust experimental set up with a large number of synthetic databases, each with a pre-defined optimal clustering solution. We measure the success of the new MOCA by investigating how often it is capable of finding the optimal solution. We compare MOCA with k-means and find some promising results. MOCA can generate a pool of clustering solutions that is more likely to contain the optimal clustering solution than the pool of solutions generated by k-means.