Multi-objective design of hierarchical consensus functions for clustering ensembles via genetic programming

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@Article{Coelho2011,

• author = "Andre L. V. Coelho and Everlandio Fernandes and Katti Faceli",
• title = "Multi-objective design of hierarchical consensus functions for clustering ensembles via genetic programming",
• journal = "Decision Support Systems",
• year = "2011",
• volume = "51",
• number = "4",
• pages = "794--809",
• ISSN = "0167-9236",
• DOI = "doi:10.1016/j.dss.2011.01.014",
• keywords = "genetic algorithms, genetic programming, Cluster analysis, Clustering ensembles, Multi-objective clustering, Hierarchical fusion, Partition selection",
• abstract = "This paper investigates a genetic programming (GP) approach aimed at the multi-objective design of hierarchical consensus functions for clustering ensembles. By this means, data partitions obtained via different clustering techniques can be continuously refined (via selection and merging) by a population of fusion hierarchies having complementary validation indices as objective functions. To assess the potential of the novel framework in terms of efficiency and effectiveness, a series of systematic experiments, involving eleven variants of the proposed GP-based algorithm and a comparison with basic as well as advanced clustering methods (of which some are clustering ensembles and/or multi-objective in nature), have been conducted on a number of artificial, benchmark and bioinformatics datasets. Overall, the results corroborate the perspective that having fusion hierarchies operating on well-chosen subsets of data partitions is a fine strategy that may yield significant gains in terms of clustering robustness.",
• notes = "Recent Advances in Data, Text, and Media Mining & Information Issues in Supply Chain and in Service System Design",

}

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

Citations