Parallel Hybrid Clustering using Genetic Programming and Multi-Objective Fitness with Density (PYRAMID)

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

  title =        "Parallel Hybrid Clustering using Genetic Programming
                 and Multi-Objective Fitness with Density ({PYRAMID})",
  author =       "Samir Tout and William Sverdlik and Junping Sun",
  booktitle =    "Proceedings of the 2006 International Conference on
                 Data Mining, {DMIN} 2006",
  publisher =    "CSREA Press",
  year =         "2006",
  editor =       "Sven F. Crone and Stefan Lessmann and 
                 Robert Stahlbock",
  ISBN =         "1-60132-004-3",
  pages =        "197--203",
  address =      "Las Vegas, Nevada, {USA}",
  month =        jun # " 26-29",
  bibdate =      "2006-12-19",
  bibsource =    "DBLP,
  keywords =     "genetic algorithms, genetic programming, Data Mining,
                 Clustering, Parallelism, Density",
  URL =          "",
  size =         "7 pages",
  abstract =     "Clustering is the process of locating patterns in
                 large data sets. It is an active research area that
                 provides value to scientific as well as business
                 applications. Practical clustering faces several
                 challenges including: identifying clusters of arbitrary
                 shapes, sensitivity to the order of input, dynamic
                 determination of the number of clusters, outlier
                 handling, processing speed of massive data sets,
                 handling higher dimensions, and dependence on
                 user-supplied parameters. Many studies have addressed
                 one or more of these challenges. This study proposes an
                 algorithm called parallel hybrid clustering using
                 genetic programming and multi-objective fitness with
                 density (PYRAMID). While still leaving significant
                 challenges unresolved, such as handling higher
                 dimensions and dependence on user-supplied parameters,
                 PYRAMID employs a combination of data parallelism, a
                 form of genetic programming, and a multiobjective
                 density-based fitness function in the context of
                 clustering to resolve most of the above challenges.
                 Preliminary experiments have yielded promising
  notes =        "Samir Tout*, William Sverdlik**, and Junping Sun*
                 *Nova Southeastern University, Fort Lauderdale,
                 Florida, USA **Eastern Michigan University, Ypsilanti,
                 Michigan, USA",

Genetic Programming entries for Samir Tout William Sverdlik Junping Sun