Clustering Approaches in Decision Making Using Fuzzy and Rough Sets

Clustering Approaches in Decision Making Using Fuzzy and Rough Sets

Deepthi P. Hudedagaddi (VIT University, India) and B. K. Tripathy (VIT University, India)
DOI: 10.4018/978-1-5225-1008-6.ch006


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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On an important decision one rarely has 100% of the information needed for a good decision no matter how much one spends or how long one waits. And, if one waits too long, he has a different problem and has to start all over. This is the terrible dilemma of the hesitant decision maker. -Robert K. Greenleaf



The notion of a set is not only basic for the whole mathematics but it also plays an important role in natural language. Sets include collections of various objects of interest, e.g., collection of books, paintings, people etc. The notion of set was proposed by G. Cantor in 1983. It was not free from controversies like that of B. Russel which questioned the definition of a set proposed by Cantor as a collection of distinct and distinguishable elements. Intuitively a set can be defined as a well- defined collection of objects called elements.

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