An Efficient Formulation of the Improved Visual Assessment of Cluster Tendency (iVAT) Algorithm

The VAT algorithm is a visual method for determining the possible number of clusters in, or the cluster tendency of a set of objects. The improved VAT (iVAT) algorithm uses a graph-theoretic distance transform to improve the effectiveness of the VAT algorithm for “tough” cases where VAT fails to accurately show the cluster tendency. In this paper, we present an efficient formulation of the iVAT algorithm which reduces the computational complexity of the iVAT algorithm from O(N3) to O(N2). We also prove a direct relationship between the VAT image and the iVAT image produced by our efficient formulation. We conclude with three examples displaying clustering tendencies in three of the Karypis data sets that illustrate the improvement offered by the iVAT transformation. We also provide a comparison of iVAT images to those produced by the Reverse Cuthill-Mckee (RCM) algorithm; our examples suggest that iVAT is superior to the RCM method of display.

• Publication year

  2012

• Venue

  IEEE Transactions on Knowledge and Data Engineering

• Publication date

  2012-05-01

• Fields of study

  Mathematics, Computer Science

• Identifiers
  DOI 10.1109/TKDE.2011.33
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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