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Inducing multi-objective clustering ensembles with genetic programming
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DOI：10.1016/j.neucom.2010.09.014.png)
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Abstract
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The recent years have witnessed a growing interest in two advanced strategies to cope with the data clustering problem namely clustering ensembles and multi-objective clustering In this paper we present a genetic programming based approach that can be considered as a hybrid of these strategies thereby allowing that different hierarchical clustering ensembles be simultaneously evolved taking into account complementary validity indices Results of computational experiments conducted with artificial and real datasets indicate that in most of the cases at least one of the Pareto optimal partitions returned by the proposed approach compares favorably or go in par with the consensual partitions yielded by two well-known clustering ensemble methods in terms of clustering quality as gauged by the corrected Rand Index (C) 2010 Elsevier B V All rights reserved
Keywords:
Cluster analysis
Ensembles
Multi objective optimization
Genetic programming
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