Abstract
In previous work, we have proposed a novel approach to data clustering based on the explicit optimization of a partitioning with respect to two complementary clustering objectives [6]. Here, we extend this idea by describing an advanced multiobjective clustering algorithm, MOCK, with the capacity to identify good solutions from the Pareto front, and to automatically determine the number of clusters in a data set. The algorithm has been subject to a thorough comparison with alternative clustering techniques and we briefly summarize these results. We then present investigations into the mechanisms at the heart of MOCK: we discuss a simple example demonstrating the synergistic effects at work in multiobjective clustering, which explain its superiority to single-objective clustering techniques, and we analyse how MOCK’s Pareto fronts compare to the performance curves obtained by single-objective algorithms run with a range of different numbers of clusters specified.
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References
Supporting material for MOCK, http://dbk.ch.umist.ac.uk/handl/mock/
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Handl, J., Knowles, J. (2005). Exploiting the Trade-off — The Benefits of Multiple Objectives in Data Clustering. In: Coello Coello, C.A., Hernández Aguirre, A., Zitzler, E. (eds) Evolutionary Multi-Criterion Optimization. EMO 2005. Lecture Notes in Computer Science, vol 3410. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-31880-4_38
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DOI: https://doi.org/10.1007/978-3-540-31880-4_38
Publisher Name: Springer, Berlin, Heidelberg
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