IEEE Systems, Man and Cybernetics Magazine - April 2020 - 10

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

by Dheeraj Kumar and James C. Bezdek

E

xploratory 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

Digital Object Identifier 10.1109/MSMC.2019.2961163
Date of current version: 20 April 2020

10

IEEE SYSTEMS, MAN, & CYBERNETICS MAGAZINE Apri l 2020

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.
Since its introduction in 2002, the VAT algorithm has
been modified by many researchers to improve the quality
of the RDI, making it applicable to various types of data
sets, such as high-volume, time-series, high-dimensional,
and streaming data, among others (collectively called the
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IEEE Systems, Man and Cybernetics Magazine - April 2020

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