British Journal of Mathematics & Computer Science, ISSN: 2231-0851,Vol.: 21, Issue.: 5
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.
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.
(1) Farouk Yalaoui, Department of Industrial Systems Engineering, Troyes University of Technology, France.
(2) Dariusz Jacek Jakóbczak, Chair of Computer Science and Management in this Department, Technical University of Koszalin, Poland.
(1) Ucuk Darusalam, Universitas Nasional, Indonesia.
(2) G. Y. Sheu, Chang-Jung Christian University, Tainan, Taiwan.
Complete Peer review History: http://www.sciencedomain.org/review-history/18685
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.
Clustering algorithms; uncertain data; traditional partitioning.
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DOI : 10.9734/BJMCS/2017/32697Review History Comments