Genetic Programming-Based Clustering Using an Information Theoretic Fitness Measure

Created by W.Langdon from gp-bibliography.bib Revision:1.4202

  author =       "Neven Boric and Pablo A. Estevez",
  title =        "Genetic Programming-Based Clustering Using an
                 Information Theoretic Fitness Measure",
  booktitle =    "2007 IEEE Congress on Evolutionary Computation",
  year =         "2007",
  editor =       "Dipti Srinivasan and Lipo Wang",
  pages =        "31--38",
  address =      "Singapore",
  month =        "25-28 " # sep,
  organization = "IEEE Computational Intelligence Society",
  publisher =    "IEEE Press",
  ISBN =         "1-4244-1340-0",
  file =         "1285.pdf",
  keywords =     "genetic algorithms, genetic programming",
  DOI =          "doi:10.1109/CEC.2007.4424451",
  abstract =     "A clustering method based on multitree genetic
                 programming and an information theoretic fitness is
                 proposed. A probabilistic interpretation is given to
                 the output of trees that does not require a conflict
                 resolution phase. The method can cluster data with
                 irregular shapes, estimate the underlying models of the
                 data for each class and use those models to classify
                 unseen patterns. The proposed scheme is tested on
                 several real and artificial data sets, outperforming
                 k-means algorithm in all of them.",
  notes =        "CEC 2007 - A joint meeting of the IEEE, the EPS, and
                 the IET.

                 IEEE Catalog Number: 07TH8963C",

Genetic Programming entries for Neven Boric Pablo A Estevez