A Literature Study on Traditional Clustering Algorithms for Uncertain Data
Journal Title: Journal of Advances in Mathematics and Computer Science - Year 2017, Vol 21, Issue 5
Abstract
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.
Authors and Affiliations
S. Sathappan, D. C. Tomar
Keywords
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- EP ID EP322015
- DOI 10.9734/BJMCS/2017/32697
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