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Uncertainty-Based Clustering Algorithms for Large Data Sets

Uncertainty-Based Clustering Algorithms for Large Data Sets
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Author(s): B. K. Tripathy (VIT University, India), Hari Seetha (Vellore Institute of Technology – Andhra Pradesh, India) and M. N. Murty (IISC Bangalore, India)
Copyright: 2018
Pages: 33
Source title: Modern Technologies for Big Data Classification and Clustering
Source Author(s)/Editor(s): Hari Seetha (Vellore Institute of Technology-Andhra Pradesh, India), M. Narasimha Murty (Indian Institute of Science, India) and B. K. Tripathy (VIT University, India)
DOI: 10.4018/978-1-5225-2805-0.ch001


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Data clustering plays a very important role in Data mining, machine learning and Image processing areas. As modern day databases have inherent uncertainties, many uncertainty-based data clustering algorithms have been developed in this direction. These algorithms are fuzzy c-means, rough c-means, intuitionistic fuzzy c-means and the means like rough fuzzy c-means, rough intuitionistic fuzzy c-means which base on hybrid models. Also, we find many variants of these algorithms which improve them in different directions like their Kernelised versions, possibilistic versions, and possibilistic Kernelised versions. However, all the above algorithms are not effective on big data for various reasons. So, researchers have been trying for the past few years to improve these algorithms in order they can be applied to cluster big data. The algorithms are relatively few in comparison to those for datasets of reasonable size. It is our aim in this chapter to present the uncertainty based clustering algorithms developed so far and proposes a few new algorithms which can be developed further.

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