A New Singular Value Decomposition Based Robust Graphical Clustering Technique and Its Application in Climatic Data

Nishith Kumar, Mohammed Nasser, Subaran Chandra Sarker

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 comparison clearly demonstrates the advantage of SVD over its competitors. Lastly the method is tested on well known Hawkins-Bradu-Kass (1984) data.


Full Text: PDF DOI: 10.5539/jgg.v3n1p227

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This work is licensed under a Creative Commons Attribution 3.0 License.

Journal of Geography and Geology   ISSN 1916-9779 (Print)   ISSN 1916-9787 (Online)

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