• DocumentCode
    693136
  • Title

    Clustering ensemble method based DILCA distance

  • Author

    Bao-Ping Su ; Ming Chunwang ; Yuan-Yuan Sun ; Kun Liu

  • Author_Institution
    Sch. of Sci., Tianjin Univ. of Technol. & Educ., Tianjin, China
  • Volume
    01
  • fYear
    2013
  • fDate
    14-17 July 2013
  • Firstpage
    29
  • Lastpage
    34
  • Abstract
    A method of clustering ensemble is transforming the clustering ensemble problem into the clustering problem among objects in a nominal information table. The basic problem is to give a method which is used to calculate the distance between the nominal attribute value. In this paper, DILCA method is adopted to calculate the distance between the nominal attribute value. Using the correlation between the attributes, this method calculate the distance more accurately. At the same time, the method uses the correlation and redundancy between attributes to decide the context attributes set of one attribute which is used to reduce the calculation quantity. The superiority of this method are demonstrated by experiments.
  • Keywords
    pattern clustering; clustering ensemble method based DILCA distance; context attributes set; nominal attribute value; nominal information table; Abstracts; Chebyshev approximation; Computers; Correlation; Ionosphere; Iris; Robustness; Clustering ensemble; DILCA; Information Gain; context;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Machine Learning and Cybernetics (ICMLC), 2013 International Conference on
  • Conference_Location
    Tianjin
  • Type

    conf

  • DOI
    10.1109/ICMLC.2013.6890439
  • Filename
    6890439