Abstract
This paper deals with various parameter reduction methods of soft set theory. It has been found that the problem of uncertainty is too complicated to solve. This paper describes how decision making problem of uncertain and incomplete data solved using soft set theory concept. There are different soft set based parameter reduction methods to solve decision making problems that give optimal solution from big data. The purpose of this article is to explore the soft set theory with its approaches for parameter reduction, classification, clustering and association of rule generations. Comparison of various soft set methods for parameters reduction in terms of computational complexity, computational time and accuracy is provided.
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