Exploratory data analysis (EDA) using data clustering is extremely important for
understanding the basic characteristics of a novel data set before developing complex …

This paper addresses the relationship between the Visual Assessment of cluster Tendency
(VAT) algorithm and single linkage hierarchical clustering. We present an analytical …

Visual methods have been widely studied and used in data cluster analysis. Given a
pairwise dissimilarity matrix\schmi D of a set of n objects, visual methods such as the VAT …

The problem of determining whether clusters are present in a data set (ie, assessment of
cluster tendency) is an important first step in cluster analysis. The visual assessment of …

Clustering tendency assessment, which aims to deduce if a dataset contains any cluster
structure, and, if it does, how many clusters it has, is a critical problem in exploratory data …

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× n image I(̃\bfD) where the …

Assessment of clustering tendency is an important first step in cluster analysis. One tool for
assessing cluster tendency is the Visual Assessment of Tendency (VAT) algorithm. VAT …

Clustering is a popular tool for exploratory data analysis. One of the major problems in
cluster analysis is the determination of the number of clusters in unlabeled data, which is a …

This paper presents a new technique for clustering either object or relational data. First, the
data are represented as a matrix D of dissimilarity values. D is reordered to D* using a visual …

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 …