A Binary Morphology-Based Clustering Algorithm Directed by Genetic Algorithm

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@InProceedings{Pedrino:2013:SMC,
  author =       "E. C. Pedrino and M. C. Nicoletti and J. H. Saito and 
                 L. M. V. Cura and V. O. Roda",
  title =        "A Binary Morphology-Based Clustering Algorithm
                 Directed by Genetic Algorithm",
  booktitle =    "IEEE International Conference on Systems, Man, and
                 Cybernetics (SMC 2013)",
  year =         "2013",
  month =        "13-16 " # oct,
  pages =        "409--414",
  keywords =     "genetic algorithms, genetic programming, cartesian
                 genetic programming, binary morphology-based
                 clustering, BMCA",
  DOI =          "doi:10.1109/SMC.2013.76",
  abstract =     "Mathematical morphology is a formalism largely used in
                 image processing for implementing many different tasks.
                 Several operators that support the formalism have also
                 been successfully used for inducing data clusters.
                 Particularly, the Binary Morphology Clustering
                 Algorithm (BMCA) is one of such inductive methods
                 which, given a set of input patterns and morphological
                 operators, produces clusters of patterns as output.
                 BMCA results, however, are dependent on suitable
                 user-defined values for the set of parameters the
                 algorithm employs namely, the resolution of its initial
                 discrimination process, the threshold associated with a
                 distance metric, the threshold associated with region
                 density and the structuring element embedded in
                 morphological operators. This paper proposes a combined
                 approach where an evolutionary algorithm is employed
                 for searching suitable parameter values for BMCA aiming
                 at producing more efficient results as far as the
                 clustering process is concerned. The proposal was
                 implemented as the system BMCAbyGA, used in several
                 successful clustering experiments described in the
                 final part of the paper. BMCAbyGA has been applied to a
                 Cartesian Genetic Programming approach for the
                 automatic construction of image Alters in hardware.",
  notes =        "Also known as \cite{6721829}",
}

Genetic Programming entries for Emerson Carlos Pedrino Maria do Carmo Nicoletti Jose Hiroki Saito Luis Mariano del Val Cura Valentin Obac Roda

Citations