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British Journal of Mathematics & Computer Science

British Journal of Mathematics & Computer Science, ISSN: 2231-0851,Vol.: 21, Issue.: 5

Review Article

A Literature Study on Traditional Clustering Algorithms for Uncertain Data

 

S. Sathappan1, S. Sridhar2* and D. C. Tomar3

1Sathyabama University, Chennai, India.

2RVCT, R V College of Engineering, Bangalore, India.

3Jerusalem College of Engineering, Chennai, India.

 

Abstracts

 

Numerous traditional Clustering algorithms for uncertain data have been proposed in the literature such as k-medoid, global kernel k-means, k-mode, u-rule, uk-means algorithm, Uncertainty-Lineage database, Fuzzy c-means algorithm. In 2003, the traditional partitioning clustering algorithm was also modified by Chau, M et al. to perform the uncertain data clustering. They presented the UK-means algorithm as a case study and illustrate how the proposed algorithm was applied. With the increasing complexity of real-world data brought by advanced sensor devices, they believed that uncertain data mining was an important and significant research area. The purpose of this paper is to present a literature study as foundation work for doing further research on traditional clustering algorithms for uncertain data, as part of PhD work of first author.

 

Keywords :

Clustering algorithms; uncertain data; traditional partitioning.

 

Full Article - PDF    Page 1-21    Article Metrics

 

DOI : 10.9734/BJMCS/2017/32697

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