A Literature Study on Traditional Clustering Algorithms for Uncertain Data

S. Sathappan

Sathyabama University, Chennai, India.

S. Sridhar *

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

D. C. Tomar

Jerusalem College of Engineering, Chennai, India.

*Author to whom correspondence should be addressed.


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.

Keywords: Clustering algorithms, uncertain data, traditional partitioning.


How to Cite

Sathappan, S., Sridhar, S., & Tomar, D. C. (2017). A Literature Study on Traditional Clustering Algorithms for Uncertain Data. Journal of Advances in Mathematics and Computer Science, 21(5), 1–21. https://doi.org/10.9734/BJMCS/2017/32697

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