Visual Approaches for Exploratory Data Analysis: A Survey of the Visual Assessment of Clustering Tendency (VAT) Family of Algorithms

Abstract

Exploratory data analysis (EDA) using data clustering is extremely important for understanding the basic characteristics of a novel data set before developing complex statistical models and testing the various hypotheses. A preliminary step to clustering is deciding whether the data contain any clusters and, if so, how many clusters to seek. This is the clustering-tendency-assessment problem, which has not received much attention in the pattern-recognition literature. An important category of algorithms in this domain includes visual approaches, represented here by the visual assessment of tendency (VAT) algorithm, which reorders the pairwise dissimilarity matrix and then generates a reordered dissimilarity image (RDI) or cluster heat map that shows possible clusters in the data by dark blocks along the diagonal.