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Khondoker, Mizanur R. 
ORCID: https://orcid.org/0000-0002-1801-1635
(2018)
Big Data Clustering.
In:
Wiley StatsRef: Statistics Reference Online.
Wiley, England.
ISBN 9781118445112
Abstract
Clustering algorithms group data items based on clearly defined similarity between the items aiming to minimize the intracluster differences and maximize the intercluster distances. A wealth of efficient and good quality clustering algorithms are already available for traditional data, but there are challenges for applying them to big data due to the overwhelming volume and complexities of such data. Data volume is getting bigger at an incredible pace due to growing access to Internet, social media, mobile devices, and technological innovations, and improving clustering algorithms, their computational cost and scalability have been the focus of much of the research in this area. This article provides an introduction to the characteristics of big data, and an overview of available algorithms and the current improvement trend of clustering algorithms for dealing with the challenges of big data.
| Item Type: | Book Section |
|---|---|
| Uncontrolled Keywords: | big data,clustering,distributed computing,hadoop,mapreduce,parallel clustering |
| Faculty \ School: | Faculty of Medicine and Health Sciences > Norwich Medical School |
| UEA Research Groups: | Faculty of Medicine and Health Sciences > Research Groups > Epidemiology and Public Health Faculty of Medicine and Health Sciences > Research Groups > Public Health and Health Services Research (former - to 2023) Faculty of Science > Research Groups > Norwich Epidemiology Centre Faculty of Medicine and Health Sciences > Research Groups > Norwich Epidemiology Centre Faculty of Medicine and Health Sciences > Research Centres > Population Health |
| Related URLs: | |
| Depositing User: | LivePure Connector |
| Date Deposited: | 14 Jun 2018 13:30 |
| Last Modified: | 19 Oct 2023 03:46 |
| URI: | https://ueaeprints.uea.ac.uk/id/eprint/67366 |
| DOI: | 10.1002/9781118445112.stat07978 |
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