• DocumentCode
    3076780
  • Title

    Particle swarm optimization methods for data clustering

  • Author

    Johnson, Ryan K. ; Sahin, Ferat

  • Author_Institution
    Rochester Inst. of Technol., Rochester, NY, USA
  • fYear
    2009
  • fDate
    2-4 Sept. 2009
  • Firstpage
    1
  • Lastpage
    6
  • Abstract
    This paper discusses the application of particle swarm optimization (PSO) to data clustering. Four different methods of PSO are tested on six test data sets and compared to k-means and fuzzy c-means. The four PSO methods, combinations of the constriction method, inertia, and the predator-prey method all out-perform k-means and fuzzy c-means in all test cases to varying degrees in terms of quantization error.
  • Keywords
    particle swarm optimisation; pattern clustering; data clustering; fuzzy c-means; k-means; particle swarm optimization; predator-prey method; quantization error; Artificial intelligence; Clustering algorithms; Cost function; Fuzzy logic; Fuzzy set theory; Fuzzy sets; Paper technology; Particle swarm optimization; Partitioning algorithms; Testing;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Soft Computing, Computing with Words and Perceptions in System Analysis, Decision and Control, 2009. ICSCCW 2009. Fifth International Conference on
  • Conference_Location
    Famagusta
  • Print_ISBN
    978-1-4244-3429-9
  • Electronic_ISBN
    978-1-4244-3428-2
  • Type

    conf

  • DOI
    10.1109/ICSCCW.2009.5379452
  • Filename
    5379452
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=3076780