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Vol.4 , No. 1, Publication Date: Apr. 29, 2019, Page: 19-23
| [1] | Amarendra Baral, Department of Mathematics, Trident Academy of Technology, F2/A, Chandaka Industrial Estate, Bhubaneswar, Odisha, India. |
| [2] | Sambunath Behera, Department of Mathematics, BCET, Sergarh, Balasore, Odisha, India. |
| [3] | Purna Chandra Nayak, Department of Mathematics, Bhadrak Autonomus College, Odisha, India. |
Rapid growth in technology and its accessibility by general public produce voluminous, heterogeneous and unstructured data resulted in the emergence of new concepts. Different computational tools such as rough-set theory, fuzzy-set theory and fuzzy-rough-set that are often applied to analyze such kind of data are the focus of this chapter. Real-life data is often vague, so fuzzy logic and rough-set theory are applied to handle uncertainty and maintain consistency in the data sets. The aim of the fuzzy-rough-based method is to generate optimum variation in the range of membership functions of linguistic variables. In this paper we have discussed the definition of Fuzzy set and Fuzzy rough set. Then we have used fuzzy set and fuzzy rough set concept in some decision making of a real life problem of uncertainty. Here we have taken fuzzy set in universe X where all concepts are described. A lower and upper approximation of acoefficient of belongingness of an object x belongs to X to a fuzzy decision concept have been described. This can be done by means of a family of fuzzy concept defined on the universal set X.
Keywords
Crisp set Fuzzy Sets, Fuzzy Rough Sets, Deterministic Rough Set, Approximation, Decision Making
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