A Novel Multi-Objective Genetic Algorithm for Clustering

Kirkland, Oliver, Rayward-Smith, Victor and de la Iglesia, Beatriz ORCID: https://orcid.org/0000-0003-2675-5826 (2011) A Novel Multi-Objective Genetic Algorithm for Clustering. In: Intelligent Data Engineering and Automated Learning - IDEAL 2011. Springer, pp. 317-326. ISBN 978-3-642-23877-2

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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.

Item Type:	Book Section
Faculty \ School:	Faculty of Science > School of Computing Sciences
UEA Research Groups:	Faculty of Science > Research Groups > Norwich Epidemiology Centre Faculty of Medicine and Health Sciences > Research Groups > Norwich Epidemiology Centre Faculty of Medicine and Health Sciences > Research Centres > Business and Local Government Data Research Centre (former - to 2023) Faculty of Science > Research Groups > Data Science and Statistics
Depositing User:	Users 2731 not found.
Date Deposited:	03 Oct 2011 12:39
Last Modified:	21 Apr 2023 06:32
URI:	https://ueaeprints.uea.ac.uk/id/eprint/34927
DOI:	10.1007/978-3-642-23878-9_38

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