Inducing multi-objective clustering ensembles with genetic programming

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@Article{Coelho2010494,

• author = "Andre L. V. Coelho and Everlandio Fernandes and Katti Faceli",
• title = "Inducing multi-objective clustering ensembles with genetic programming",
• journal = "Neurocomputing",
• volume = "74",
• number = "1-3",
• pages = "494--498",
• year = "2010",
• note = "Artificial Brains",
• ISSN = "0925-2312",
• DOI = "doi:10.1016/j.neucom.2010.09.014",
• URL = "http://www.sciencedirect.com/science/article/B6V10-517YN4X-P/2/7322b78e25061d5ecbaa12f058216cd0",
• keywords = "genetic algorithms, genetic programming, Cluster analysis, Ensembles, Multi-objective optimization",
• abstract = "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 favourably 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.",

}

Genetic Programming entries for Andre Luis Vasconcelos Coelho Everlandio Reboucas Queiroz Fernandes Katti Faceli

Citations