Application of Multivariate-Rank-Based Techniques in Clustering of Big Data
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...
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doaj-b69b416f71824be49a512ec427a2b7f42021-04-02T11:41:32ZengSAGE PublishingVikalpa0256-09092018-12-014310.1177/0256090918804385Application of Multivariate-Rank-Based Techniques in Clustering of Big DataPritha Guha0 is an Assistant Professor in the Institute of Management, Nirma University. She received her PhD in statistics from School of Mathematics, University of Birmingham, UK. She also received her MSc (by research) in statistics from the Department of Statistics and Applied Probability, National University of Singapore, and MSc in mathematics from IIT Kanpur. Her current research interest includes multivariate statistics and clustering of big data. E-mail: 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.https://doi.org/10.1177/0256090918804385 |
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DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Pritha Guha |
spellingShingle |
Pritha Guha Application of Multivariate-Rank-Based Techniques in Clustering of Big Data Vikalpa |
author_facet |
Pritha Guha |
author_sort |
Pritha Guha |
title |
Application of Multivariate-Rank-Based Techniques in Clustering of Big Data |
title_short |
Application of Multivariate-Rank-Based Techniques in Clustering of Big Data |
title_full |
Application of Multivariate-Rank-Based Techniques in Clustering of Big Data |
title_fullStr |
Application of Multivariate-Rank-Based Techniques in Clustering of Big Data |
title_full_unstemmed |
Application of Multivariate-Rank-Based Techniques in Clustering of Big Data |
title_sort |
application of multivariate-rank-based techniques in clustering of big data |
publisher |
SAGE Publishing |
series |
Vikalpa |
issn |
0256-0909 |
publishDate |
2018-12-01 |
description |
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. |
url |
https://doi.org/10.1177/0256090918804385 |
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AT prithaguha applicationofmultivariaterankbasedtechniquesinclusteringofbigdata |
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