• DocumentCode
    2709136
  • Title

    ReDSOM: Relative Density Visualization of Temporal Changes in Cluster Structures Using Self-Organizing Maps

  • Author

    Denny ; Williams, Graham J. ; Christen, Peter

  • Author_Institution
    Dept. of Comput. Sci., Australian Nat. Univ., Canberra, ACT
  • fYear
    2008
  • fDate
    15-19 Dec. 2008
  • Firstpage
    173
  • Lastpage
    182
  • Abstract
    We introduce a self-organizing map (SOM) based visualization method that compares cluster structures in temporal datasets using relative density SOM (ReDSOM) visualization. Our method, combined with a distance matrix-based visualization, is capable of visually identifying emerging clusters, disappearing clusters, enlarging clusters, contracting clusters, the shifting of cluster centroids, and changes in cluster density. For example, when a region in a SOM becomes significantly more dense compared to an earlier SOM, and well separated from other regions, then the new region can be said to represent a new cluster. The capabilities of ReDSOM are demonstrated using synthetic datasets, as well as real-life datasets from the World Bank and the Australian Taxation Office. The results on the real-life datasets demonstrate that changes identified interactively can be related to actual changes. The identification of such cluster changes is important in many contexts, including the exploration of changes in population behavior in the context of compliance and fraud in taxation.
  • Keywords
    data mining; data visualisation; learning (artificial intelligence); pattern clustering; self-organising feature maps; ReDSOM; cluster structure; data mining; distance matrix-based visualization; relative density visualization; self-organizing map; temporal change visualization; temporal dataset; Australia; Change detection algorithms; Computer science; Data analysis; Data mining; Data visualization; Degradation; Government; Self organizing feature maps; Supervised learning; Self-Organizing Map; Temporal Cluster Analysis; change analysis; visual analytics;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Data Mining, 2008. ICDM '08. Eighth IEEE International Conference on
  • Conference_Location
    Pisa
  • ISSN
    1550-4786
  • Print_ISBN
    978-0-7695-3502-9
  • Type

    conf

  • DOI
    10.1109/ICDM.2008.34
  • Filename
    4781112