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

Created by W.Langdon from gp-bibliography.bib Revision:1.4549

  author =       "Andre L. V. Coelho and Everlandio Fernandes and 
                 Katti Faceli",
  title =        "Multi-objective design of hierarchical consensus
                 functions for clustering ensembles via genetic
  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