Inducing multi-objective clustering ensembles with genetic programming

Created by W.Langdon from gp-bibliography.bib Revision:1.4340

  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 =          "",
  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

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