• DocumentCode
    401631
  • Title

    A modified PCM clustering algorithm

  • Author

    Li, Kai ; Huang, Hou-kuan ; Li, Kun-lun

  • Author_Institution
    Sch. of Comput. & Inf. Technol., Northern Jiaotong Univ., Beijing, China
  • Volume
    2
  • fYear
    2003
  • fDate
    2-5 Nov. 2003
  • Firstpage
    1174
  • Abstract
    A fast PCM clustering algorithm is proposed in this paper. First, the fuzzy and possibilistic c-means (FCM and PCM ) clustering algorithms are analyzed and some drawbacks and limitations are pointed out. Second, based on the reformulation theorem, by means of modifying PCM model, an effective and efficient clustering algorithm is proposed here, which is referred to as a modified PCM clustering (MPCM). As eliminating the computation of membership parameters in each iteration, this algorithm saves an amount of running time. Finally, experiments are implemented by using MPCM clustering algorithm, and the chosen methods of parameters are also discussed. Experiments show that MPCM has not only abilities of resisting noise and avoiding trivial solution, but has fast clustering ability.
  • Keywords
    fuzzy set theory; probability; fuzzy c-means; membership parameters; modified PCM clustering algorithm; noise; objective function; possibilistic c-means; reformulation theorem; trivial solution avoidance; Algorithm design and analysis; Clustering algorithms; Computer science; Computer vision; Differential equations; Information technology; Machine learning algorithms; Mathematics; Phase change materials; Prototypes;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Machine Learning and Cybernetics, 2003 International Conference on
  • Print_ISBN
    0-7803-8131-9
  • Type

    conf

  • DOI
    10.1109/ICMLC.2003.1259663
  • Filename
    1259663