A Hybrid Approach to Cluster Detection

Created by W.Langdon from gp-bibliography.bib Revision:1.4333

  title =        "A Hybrid Approach to Cluster Detection",
  author =       "Samir Tout and Junping Sun and William Sverdlik",
  journal =      "IAENG International Journal of Computer Science",
  year =         "2007",
  volume =       "34",
  number =       "1",
  month =        "15 " # aug,
  keywords =     "genetic algorithms, genetic programming, Data Mining,
                 Clustering, Density, Parallelism",
  ISSN =         "1819-9224",
  URL =          "http://www.iaeng.org/IJCS/issues_v34/issue_1/IJCS_34_1_14.pdf",
  URL =          "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=",
  bibsource =    "OAI-PMH server at citeseerx.ist.psu.edu",
  contributor =  "CiteSeerX",
  language =     "en",
  oai =          "oai:CiteSeerXPSU:",
  abstract =     "---Recent technological advances require computer
                 algorithms that can effectively analyze and classify
                 data on a large scale that was unachievable just a few
                 years ago. For instance, in response to a query,
                 commercial search engines routinely consider web pages
                 amounting into billions while genomic searches may deal
                 with a search space of a similar or even higher
                 magnitude. Clustering algorithms are an ideal choice to
                 quickly categorize data; they are conceptually simple
                 and require little background knowledge. Many
                 clustering algorithms have been introduced in recent
                 decades; but each approach brought along new challenges
                 to consider, such as outlier handling, detection of
                 arbitrary shaped clusters, processing speed, and
                 dependence on user supplied parameters. PYRAMID, or
                 parallel hybrid clustering using genetic programming
                 and multiobjective fitness with density, is a
                 clustering algorithm that we introduced in a previous
                 research. It addresses several of the above challenges
                 by using a combination of data parallelism, a form of
                 genetic programming, and a multi-objective
                 density-based fitness function. This paper summarizes
                 some of the characteristics of PYRAMID along with
                 experiments that were performed on multiple challenging
                 datasets. Empirical results derived from these
                 experiments are presented and future directions are
  notes =        "Samir Tout is a consultant with Keane, Inc., 24901
                 Northwestern Hwy, Southfield, MI 48075 and an adjunct
                 professor at the Department of Computer Science,
                 Eastern Michigan University, Ypsilanti, MI,

                 Junping Sun is a professor at the Graduate School of
                 Computer and Information Sciences, Nova Southeastern
                 University, 3301 College Avenue, Fort Lauderdale,
                 Florida 33314, USA

                 William Sverdlik is an associate professor at the
                 Department of Computer Science, Eastern Michigan
                 University, Ypsilanti, MI, 48197",

Genetic Programming entries for Samir Tout Junping Sun William Sverdlik