• DocumentCode
    671515
  • Title

    A new bi-clustering approach using topological maps

  • Author

    Chaibi, Amine ; Lebbah, Mustapha ; Azzag, Hanane

  • Author_Institution
    LIPN, Univ. of Paris 13, Villetaneuse, France
  • fYear
    2013
  • fDate
    4-9 Aug. 2013
  • Firstpage
    1
  • Lastpage
    7
  • Abstract
    In this paper, we propose a new bi-clustering algorithm based on self-organizing maps titled BiTM (Bi-clustering using Topological Map). BiTM provides a simultaneous clustering of rows and columns of the data matrix in order to increase the homogeneity of bi-clusters by respecting neighborhood relationship and using a single map. BiTM maps provide a new topological visualization of the bi-clusters. Experimental results and comparison studies show that BiTM improves the results in term of bi-clustering and visualization.
  • Keywords
    data visualisation; pattern clustering; self-organising feature maps; BiTM maps; biclustering using topological map; column clustering; data matrix; neighborhood relationship; row clustering; self-organizing maps; topological visualization; Breast; Cancer; Glass; Heart; Indexes; Prototypes; Sonar; Bi-clustering; self-organizing maps; visualization;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Neural Networks (IJCNN), The 2013 International Joint Conference on
  • Conference_Location
    Dallas, TX
  • ISSN
    2161-4393
  • Print_ISBN
    978-1-4673-6128-6
  • Type

    conf

  • DOI
    10.1109/IJCNN.2013.6706855
  • Filename
    6706855