Exploratory Hot Spot Profile Analysis Using Interactive Visual Drill-Down Self-Organizing Maps

  • Denny
  • Graham J. Williams
  • Peter Christen
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5012)


Real-life datasets often contain small clusters of unusual sub-populations. These clusters, or ‘hot spots’, are usually sparse and of special interest to an analyst. We present a methodology for identifying hot spots and ranking attributes that distinguish them interactively, using visual drill-down Self-Organizing Maps. The methodology is particularly useful for understanding hot spots in high dimensional datasets. Our approach is demonstrated using a large real life taxation dataset.


self-organizing maps hot spot analysis attribute ranking imbalanced data interactive drill-down visualization 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Denny
    • 1
    • 2
  • Graham J. Williams
    • 3
    • 1
  • Peter Christen
    • 1
  1. 1.Department of Computer ScienceThe Australian National UniversityAustralia
  2. 2.Faculty of Computer ScienceUniversity of IndonesiaIndonesia
  3. 3.The Australian Taxation Office 

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