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

Kumar, Dheeraj; Bezdek, James C.

doi:10.1109/msmc.2019.2961163

Cited by 27 publications

(23 citation statements)

References 161 publications

(172 reference statements)

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“…The well-known Visual Assessment of Tendency (VAT) ( Bezdek and Hathaway, 2002 , Kumar and Bezdek, 2020 ) representation is used here to visualize the clusters formed by the clustering methods. This technique generally represents pairwise dissimilarity information of n data objects as an n × n image, where the data objects are reordered in such a way so that the resulting image is able to highlight potential cluster structure in the dataset.…”

Section: Methodsmentioning

confidence: 99%

“…Moreover, their presence in different countries are identified along with the evolution of the virus genomes from January to July 2020 for each of the 73 countries. These outcomes are shown quantitatively and visually through BioCircos ( Cui et al, 2016 ), Visual Assessment of Tendency (VAT) plot ( Bezdek and Hathaway, 2002 , Kumar and Bezdek, 2020 ) and Heatmap ( Deng et al, 2014 ). Therefore, the major contributions of this work can be summarised as: SNP identification from 10664 SARS-CoV-2 genomes of 73 countries, binary dataset creation from SNP data to find the number of clusters as virus strains present in 73 countries around the globe and their evolution, identifying signature SNPs in each cluster and determining the structural stability of the non-synonymous signature SNPs to judge the characteristics of the identified clusters.…”

Section: Introductionmentioning

confidence: 99%

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Genome-wide analysis of 10664 SARS-CoV-2 genomes to identify virus strains in 73 countries based on single nucleotide polymorphism

et al. 2021

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Since the onslaught of SARS-CoV-2, the research community has been searching for a vaccine to fight against this virus. However, during this period, the virus has mutated to adapt to the different environmental conditions in the world and made the task of vaccine design more challenging. In this situation, the identification of virus strains is very much timely and important task. We have performed genome-wide analysis of 10664 SARS-CoV-2 genomes of 73 countries to identify and prepare a Single Nucleotide Polymorphism (SNP) dataset of SARS-CoV-2. Thereafter, with the use of this SNP data, the advantage of hierarchical clustering is taken care of in such a way so that Average Linkage and Complete Linkage with Jaccard and Hamming distance functions are applied separately in order to identify the virus strains as clusters present in the SNP data. In this regard, the consensus of both the clustering results are also considered while Silhouette index is used as a cluster validity index to measure the goodness of the clusters as well to determine the number of clusters or virus strains. As a result, we have identified five major clusters or virus strains present worldwide. Apart from quantitative measures, these clusters are also visualized using Visual Assessment of Tendency (VAT) plot. The evolution of these clusters are also shown. Furthermore, top 10 signature SNPs are identified in each cluster and the non-synonymous signature SNPs are visualised in the respective protein structures. Also, the sequence and structural homology-based prediction along with the protein structural stability of these non-synonymous signature SNPs are reported in order to judge the characteristics of the identified clusters. As a consequence, T85I, Q57H and R203M in NSP2, ORF3a, Nucleocapsid respectively are found to be responsible for Cluster 1 as they are damaging and unstable non-synonymous signature SNPs. Similarly, F506L and S507C in Exon are responsible for both Clusters 3 and 4 while Clusters 2 and 5 do not exhibit such behaviour due to the absence of any non-synonymous signature SNPs. In addition to all these, the code, SNP dataset, 10664 labelled SARS-CoV-2 strains and additional results as supplementary are provided through our website for further use.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Genome-wide analysis of 10664 SARS-CoV-2 genomes to identify virus strains in 73 countries based on single nucleotide polymorphism

et al. 2021

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“…(Pushpalatha et al 2020 ; Devisetty et al 2019 ) of text documents. Many pre-cluster tendency methods are surveyed (Mahallati et al 2019 ; Hu and Hathaway 2008 ; Park et al 2016 ; Kumar et al 2015 ; Kumar and Bezdek 2020 ; Varish et al 2020 ) for finding the cluster tendency. It is recognized that VAT and iVAT (Havens and Bezdek 2011 ) are the best choices for finding the assessment of cluster tendency, whereas VAT and iVAT are suited for normal and path-shaped datasets respectively.…”

Section: Related Visual Methodsmentioning

confidence: 99%

An effective assessment of cluster tendency through sampling based multi-viewpoints visual method

Prasad¹,

Reddy

Mohammed

2021

J Ambient Intell Human Comput

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“…This dataset may be presented in the form of a dissimilarity matrix DN = [dij], where dij represents dissimilarity between oi and oj. Before applying a clustering algorithm to this dataset, it must be decided whether it contains meaningful clusters or non-random structures through the assessment of cluster tendency [1,5]. This is an important issue with unsupervised learning, because, clustering methods will estimate arbitrary c (1 ≤ c ≤ N ) clusters, even when there are no apparent clusters, to satisfy the constraints of the algorithm.…”

Section: Introductionmentioning

confidence: 99%

“…This is an important issue with unsupervised learning, because, clustering methods will estimate arbitrary c (1 ≤ c ≤ N ) clusters, even when there are no apparent clusters, to satisfy the constraints of the algorithm. This problem can be addressed by a simple and intuitive visual approach called the Visual Assessment of (cluster) Tendency (VAT) [5,6].…”

Section: Introductionmentioning

confidence: 99%

Shapelet Based Visual Assessment of Cluster Tendency in Analyzing Complex Upper Limb Motion

Datta

Karmakar

Rathore

et al. 2021

ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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The evolution of ubiquitous sensors has led to the generation of copious amounts of waveform data. Human motion waveform analysis has found significance in clinical and home-based activity monitoring. Exploration of cluster structure in such waveform data prior to developing learning models is an important pattern recognition problem. A prominent category of algorithms in this direction, known as Visual Assessment of (cluster) Tendency (VAT), employs visual approaches to study cluster evolution through heat maps. This paper proposes shape-iVAT, a new relative of an improved VAT model, that captures local time-series characteristics through representative subsequences, known as shapelets, to identify interesting patterns in motion data. We propose an unsupervised method for shapelet extraction using maximin shape sampling and shapebased distance computation for selecting key shapelets representing characteristic motion patterns. These shapelets are used to transform waveform data into a dissimilarity matrix for VAT evaluation. We demonstrate that the proposed method outperforms standard VAT with global distance measures for identifying complex upper limb motion captured using a camera-based motion sensing device. We also show that our method has significance in efficient and interpretable cluster tendency assessment for anomaly detection and continuous motion monitoring.

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

Cited by 27 publications

References 161 publications

Genome-wide analysis of 10664 SARS-CoV-2 genomes to identify virus strains in 73 countries based on single nucleotide polymorphism

Genome-wide analysis of 10664 SARS-CoV-2 genomes to identify virus strains in 73 countries based on single nucleotide polymorphism

An effective assessment of cluster tendency through sampling based multi-viewpoints visual method

Shapelet Based Visual Assessment of Cluster Tendency in Analyzing Complex Upper Limb Motion

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