iVAT and aVAT: Enhanced Visual Analysis for Cluster Tendency Assessment

Liang Wang, U T V Nguyen, J C Bezdek, C A Leckie, K Ramamohanarao

Research output: Chapter or section in a book/report/conference proceedingChapter or section

53 Citations (SciVal)


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 x n image I((D) over tilde) 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 high-light cluster structure in I((D) over tilde) 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 I((D) over tilde). Experimental results on several synthetic and real-world data sets have demonstrated the effectiveness of our methods.
Original languageEnglish
Title of host publicationAdvances in Knowledge Discovery and Data Mining, Pt I, Proceedings
EditorsM J Zaki, J X Yu, B Ravindran, V Pudi
Number of pages12
ISBN (Print)9783642136566
Publication statusPublished - 2010
Event14th Pacific-Asia Conference on Knowledge Discovery and Data Mining - Hyderabad, India
Duration: 21 Jun 201024 Jun 2010

Publication series

NameLecture Notes in Artificial Intelligence


Conference14th Pacific-Asia Conference on Knowledge Discovery and Data Mining
Abbreviated titlePAKDD 2010

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