Clustering Massive High Dimensional Data with Dynamic Feature Maps

  • Rasika Amarasiri
  • Damminda Alahakoon
  • Kate Smith-Miles
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4233)


This paper presents an algorithm based on the Growing Self Organizing Map (GSOM) called the High Dimensional Growing Self Organizing Map with Randomness (HDGSOMr) that can cluster massive high dimensional data efficiently. The original GSOM algorithm is altered to accommodate for the issues related to massive high dimensional data. These modifications are presented in detail with experimental results of a massive real-world dataset.


Learning Rate Boundary Node Spread Factor Growth Threshold Massive Dataset 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Rasika Amarasiri
    • 1
  • Damminda Alahakoon
    • 1
  • Kate Smith-Miles
    • 2
  1. 1.Clayton School of Information TechnologyMonash UniversityAustralia
  2. 2.School of Engineering and Information TechnologyDeakin UniversityBurwoodAustralia

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