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Abstract. Soft set theory is a new general mathematical method for dealing with uncertain data which proposed by Molodtsov in 1999 had been applied by researchers in decision making problems. However, most existing studies generated exact solution that should be soft solution because the determination of the initial problem only uses values or language approach. This paper shows the use of soft set theory as a generic mathematical tool to describe the objects in the form of information systems and evaluate using multidimensional scaling techniques to find the soft solution and recommendation for making a decision.

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The core concept of classical rough sets are clustering similarities and dissimilarities of objects based on the notions of indiscernibility and discernibility. In this paper, we present a new method of clustering data based on the combination of indiscernibility and its indiscernibility level. The indiscernibility level quantifies the indiscernibility of pairs of objects among other objects in information systems. The result of this paper show the dual notions of indiscernibility and its indiscernibility level play an important role in clustering information systems.

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