A Genetic Programming Approach to Data Clustering

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@InProceedings{conf/fgit/AhnOO11,
  author =       "Chang Wook Ahn and Sanghoun Oh and Moonyoung Oh",
  title =        "A Genetic Programming Approach to Data Clustering",
  booktitle =    "Proceedings of the International Conference on
                 Multimedia, Computer Graphics and Broadcasting (MulGraB
                 2011) Part {II}",
  editor =       "Tai-Hoon Kim and Hojjat Adeli and 
                 William I. Grosky and Niki Pissinou and Timothy K. Shih and 
                 Edward J. Rothwell and Byeong Ho Kang and Seung-Jung Shin",
  year =         "2011",
  volume =       "263",
  series =       "Communications in Computer and Information Science",
  pages =        "123--132",
  address =      "Jeju Island, Korea",
  month =        dec # " 8-10",
  publisher =    "Springer",
  note =         "Held as Part of the Future Generation Information
                 Technology Conference, {FGIT} 2011, in Conjunction with
                 {GDC} 2011",
  keywords =     "genetic algorithms, genetic programming",
  isbn13 =       "978-3-642-27186-1",
  DOI =          "doi:10.1007/978-3-642-27186-1_15",
  size =         "10 pages",
  abstract =     "This paper presents a genetic programming (GP) to data
                 clustering. The aim is to accurately classify a set of
                 input data into their genuine clusters. The idea lies
                 in discovering a mathematical function on clustering
                 regularities and then use the rule to make a correct
                 decision on the entities of each cluster. To this end,
                 GP is incorporated into the clustering procedures. Each
                 individual is represented by a parsing tree on the
                 program set. Fitness function evaluates the quality of
                 clustering with regard to similarity criteria.
                 Crossover exchanges sub-trees between parental
                 candidates in a positionally independent fashion.
                 Mutation introduces (in part) a new sub-tree with a low
                 probability. The variation operators (i.e., crossover,
                 mutation) offer an effective search capability to
                 obtain the improved quality of solution and the
                 enhanced speed of convergence. Experimental results
                 demonstrate that the proposed approach outperforms a
                 well-known reference.",
  affiliation =  "School of Information & Communication Engineering,
                 Sungkyunkwan University, Suwon, 440-746 Korea",
  bibdate =      "2011-12-08",
  bibsource =    "DBLP,
                 http://dblp.uni-trier.de/db/conf/fgit/mulgrab2011-2.html#AhnOO11",
}

Genetic Programming entries for Chang Wook Ahn Sanghoun Oh Moonyoung Oh

Citations