Enhanced visual analysis for cluster tendency assessment and data partitioning

Abstract

Visual methods have been widely studied and used in data cluster analysis. Given a pairwise dissimilarity matrix D of a set of n objects, visual methods such as the VAT algorithm generally represent D as an n × n image (D̃) 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 their inability to highlight cluster structure when D contains highly complex clusters. This paper addresses this limitation by proposing a Spectral VAT algorithm, where D is mapped to D′ in a graph embedding space and then reordered to D̃′ using the VAT algorithm. A strategy for automatic determination of the number of c..