2011
DOI: 10.5539/jgg.v3n1p227
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A New Singular Value Decomposition Based Robust Graphical Clustering Technique and Its Application in Climatic Data

Abstract:

An attempt is made to study mathematical properties of singular value decomposition (SVD) and its data exploring capacity and to apply them to make exploratory type clustering for 10 climatic variables and thirty weather stations in Bangladesh using a newly developed graphical technique. Findings in SVD and Robust singular value decomposition (RSVD) based graphs are compared with that of classical K-means cluster analysis, its robust version, partition by medoids (PAM) and classical factor analysis, and the… Show more

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Cited by 2 publications
(3 citation statements)
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“…Further detail can be found in Hawkins et al and a nice flowchart of the algorithm in Kumar et al. , p234…”
Section: Robust Singular Spectrum Analysismentioning
confidence: 99%
“…Further detail can be found in Hawkins et al and a nice flowchart of the algorithm in Kumar et al. , p234…”
Section: Robust Singular Spectrum Analysismentioning
confidence: 99%
“…However, as already indicated by several authors including Kumar, Nasser and Sarker (2011); Hawkins, Liu and Young (2001); Liu et al (2003), the usual method of computing SVD is highly susceptible to outliers in the data matrix. As the data are becoming increasingly vast and complex in the present era, it is also being susceptible to the inclusion of different forms of noises, corruptions and contamination by outlying observations.…”
mentioning
confidence: 95%
“…Moreover, SVD is recently being used extensively to solve different problems in bioinformatics, which include the analysis of protein functional associations (Franceschini et al, 2016), clustering for gene expression analysis (Horn and Axel, 2003;Liang, 2007;Bustamam, Formalidin and Siswantining, 2018), protein coding region prediction (Das, Das and Nanda, 2017), etc. In geographical science as well, Kumar, Nasser and Sarker (2011) used SVD based techniques (in fact, its robust version) in order to find proper graphical representations of climate data, mitigating the effect of outlying thunderstorms and heavy rains. Such a wide range of applications clearly underscores the relevance of SVD as an extremely integral component of data analysis across a multitude of disciplines.…”
mentioning
confidence: 99%