A Novel Multi-Objective Genetic Algorithm for Clustering

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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.
Original languageEnglish
Title of host publicationIntelligent Data Engineering and Automated Learning - IDEAL 2011
EditorsHujun Yin, Wenjia Wang, Victor Rayward-Smith
Number of pages10
ISBN (Electronic)978-3-642-23878-9
ISBN (Print)978-3-642-23877-2
Publication statusPublished - 2011

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