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Cluster Validity Using Support Vector Machines

  • Vladimir Estivill-Castro
  • Jianhua Yang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2737)

Abstract

Gaining confidence that a clustering algorithm has produced meaningful results and not an accident of its usually heuristic optimization is central to data analysis. This is the issue of validity and we propose here a method by which Support Vector Machines are used to evaluate the separation in the clustering results. However, we not only obtain a method to compare clustering results from different algorithms or different runs of the same algorithm, but we can also filter noise and outliers. Thus, for a fixed data set we can identify what is the most robust and potentially meaningful clustering result. A set of experiments illustrates the steps of our approach.

Keywords

Support Vector Machine Support Vector Cluster Result Cluster Structure Potential Outlier 
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 2003

Authors and Affiliations

  • Vladimir Estivill-Castro
    • 1
  • Jianhua Yang
    • 2
  1. 1.Griffith UniversityBrisbaneAustralia
  2. 2.The University of Western SydneyCampbelltownAustralia

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