iVAT and aVAT: enhanced visual analysis for cluster tendency assessment

  • Authors:

  • Liang Wang;Uyen T. V. Nguyen;James C. Bezdek;Christopher A. Leckie;Kotagiri Ramamohanarao

  • Affiliations:

  • Department of Computer Science, University of Bath, BA2 7AY, United Kingdom;Department of Computer Science and Software Engineering, The University of Melbourne, Victoria, Australia;Department of Computer Science and Software Engineering, The University of Melbourne, Victoria, Australia;Department of Computer Science and Software Engineering, The University of Melbourne, Victoria, Australia;Department of Computer Science and Software Engineering, The University of Melbourne, Victoria, Australia

  • Venue:
  • PAKDD'10 Proceedings of the 14th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part I
  • Year:
  • 2010

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Abstract

Given a pairwise dissimilarity matrix D of a set of n objects, visual methods (such as VAT) for cluster tendency assessment generally represent D as an n×n image $\mathrm{I}(\tilde{\bf D})$ where the objects are reordered to reveal hidden cluster structure as dark blocks along the diagonal of the image. A major limitation of such methods is the inability to highlight cluster structure in $\mathrm{I}(\tilde{\bf D})$ when D contains highly complex clusters. To address this problem, this paper proposes an improved VAT (iVAT) method by combining a path-based distance transform with VAT. In addition, an automated VAT (aVAT) method is also proposed to automatically determine the number of clusters from $\mathrm{I}(\tilde{\bf D})$. Experimental results on several synthetic and real-world data sets have demonstrated the effectiveness of our methods.