Dunn's cluster validity index as a contrast measure of VAT images

This paper addresses the relationship between the Visual Assessment of cluster Tendency (VAT) algorithm and Dunn’s cluster validity index. We present an analytical comparison in conjunction with numerical examples to demonstrate that the effectiveness of VAT in showing cluster tendency is directly related to Dunn’s index. This analysis is important to understanding the underlying theory of VAT and VAT-based algorithms and, more generally, other algorithms that are based on, or similar to, Prim’s Algorithm.

• Type: Conference
• Year: 2008
• Where: ICPR
• Authors: Timothy C. Havens, James C. Bezdek, James M. Keller, Mihail Popescu
• Keywords: Algorithms, Cluster Tendency, Computer Vision, Dunn’s Cluster Validity, ICPR 2008