Clustering Approaches in Decision Making Using Fuzzy and Rough Sets

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Author(s): Deepthi P. Hudedagaddi (VIT University, India)and B. K. Tripathy (VIT University, India)
Copyright: 2017
Pages: 21
Source title: Handbook of Research on Fuzzy and Rough Set Theory in Organizational Decision Making
Source Author(s)/Editor(s): Arun Kumar Sangaiah (VIT University, India), Xiao-Zhi Gao (University of Eastern Finland, Finland)and Ajith Abraham (Machine Intelligence Research Labs, USA)
DOI: 10.4018/978-1-5225-1008-6.ch006

Keywords: Business & Management / Business and Organizational Research / Business Science Reference / Decision Support Systems

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Abstract

Data clustering has been an integral and important part of data mining. It has wide applications in database anonymization, decision making, image processing and pattern recognition, medical diagnosis and geographical information systems, only to name a few. Data in real life scenario are having imprecision inherent in them. So, early crisp clustering techniques are very less efficient. Several imprecision based models have been proposed over the years like the fuzzy sets, rough sets, intuitionistic fuzzy sets and many of their generalized versions. Of late, it has been established that the hybrid models obtained as combination of these imprecise models are far more efficient than the individual ones. So, many clustering algorithms have been put forth using these hybrid models. The focus of this chapter is to discuss on some of the data clustering algorithms developed so far and their applications mainly in the area of decision making.

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Clustering Approaches in Decision Making Using Fuzzy and Rough Sets

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