An Algorithm for Adaptive Clustering and Visualisation of Highdimensional Data Sets

  • F. Schwenker
  • H. A. Kestler
  • G. Palm
Conference paper
Part of the International Centre for Mechanical Sciences book series (CISM, volume 408)


We describe an algorithm for exploratory data analysis which combines adaptive c-means clustering and multi-dimensional scaling (ACMDS). ACMDS is an algorithm for the online visualization of clustering processes and may be considered as an alternative approach to Kohonen’s self organizing feature map (SOM). Whereas SOM is a heuristic neural network algorithm, ACMDS is derived from multivariate statistical algorithms. The implications of ACMDS are illustrated through five different data sets.


Sudden Cardiac Death Cluster Center Representation Center Projection Center Handwritten Digit 
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Copyright information

© Springer-Verlag Wien 2000

Authors and Affiliations

  • F. Schwenker
    • 1
  • H. A. Kestler
    • 2
    • 3
  • G. Palm
    • 4
  1. 1.University of UlmUlmGermany
  2. 2.University of UlmUlmGermany
  3. 3.University Hospital UlmUlmGermany
  4. 4.University of UlmUlmGermany

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