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

Pritha Guha

Vikalpa: The Journal for Decision Makers, 2018, vol. 43, issue 4, 179-190

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.

Keywords: Big Data; Cluster Analysis; Data Depth; Non-parametric Inference; Spatial Rank Function (search for similar items in EconPapers)
Date: 2018
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:sae:vikjou:v:43:y:2018:i:4:p:179-190

DOI: 10.1177/0256090918804385

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