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Application of Multivariate-Rank-Based Techniques in Clustering of Big Data

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  • Pritha Guha

Abstract

Executive Summary Very large or complex data sets, which are difficult to process or analyse using traditional data handling techniques, are usually referred to as big data. The idea of big data is characterized by the three ‘v’s which are volume , velocity , and variety ( Liu, McGree, Ge, & Xie, 2015 ) referring respectively to the volume of data, the velocity at which the data are processed and the wide varieties in which big data are available. Every single day, different sectors such as credit risk management, healthcare, media, retail, retail banking, climate prediction, DNA analysis and, sports generate petabytes of data (1 petabyte = 250 bytes). Even basic handling of big data, therefore, poses significant challenges, one of them being organizing the data in such a way that it can give better insights into analysing and decision-making. With the explosion of data in our life, it has become very important to use statistical tools to analyse them.

Suggested Citation

  • Pritha Guha, 2018. "Application of Multivariate-Rank-Based Techniques in Clustering of Big Data," Vikalpa: The Journal for Decision Makers, , vol. 43(4), pages 179-190, December.
    • Handle: RePEc:sae:vikjou:v:43:y:2018:i:4:p:179-190
    DOI: 10.1177/0256090918804385
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    References listed on IDEAS

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    1. J. Gower & P. Legendre, 1986. "Metric and Euclidean properties of dissimilarity coefficients," Journal of Classification, Springer;The Classification Society, vol. 3(1), pages 5-48, March.
    2. Robert Tibshirani & Guenther Walther & Trevor Hastie, 2001. "Estimating the number of clusters in a data set via the gap statistic," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 63(2), pages 411-423.
    3. Fraley C. & Raftery A.E., 2002. "Model-Based Clustering, Discriminant Analysis, and Density Estimation," Journal of the American Statistical Association, American Statistical Association, vol. 97, pages 611-631, June.
    4. Anil K. Ghosh & Probal Chaudhuri, 2005. "On Maximum Depth and Related Classifiers," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 32(2), pages 327-350, June.
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