Once the information table is obtained, the data is discretised into partitions as mentioned earlier. An information system can be understood by a pairΛ = (U, A**), where U and A,** are finite, non-empty sets called the universe, and the set of attributes, respectively (Deja and Peszek, 2003). For every attribute a an element of A, we associate a set Va**, of its** values, where Va **is called the value set of** a.

a: U→ Va **(1)** 
Any subset B of A determines a binary relation *I(B)* on U**, which is called an** indiscernibility relation. The main concept of rough set theory is an indiscernibility relation (indiscernibility meaning indistinguishable from one another). Sets that are indiscernible are called elementary sets, and these are considered the building blocks of RST's knowledge of reality. A union of elementary sets is called a crisp set, while any other sets are referred to as rough or vague. More formally, for given information system Λ, then for any subset B ⊆ A, there is an associated equivalence relation *I(B)*
called the B − *indiscernibility* **relation and is represented as shown as:** 
(x, y)∈ I(B) iff a(x) = a( y**) (2)** 
RST offers a tool to deal with indiscernibility and the way in which it works is, for each concept/decision X, the greatest definable set containing X **and the least definable set** containing X **are computed. These two sets are called the lower and upper approximation** respectively. The sets of cases/objects with the same outcome variable are assembled together. This is done by looking at the "purity" of the particular objects attributes in relation to its outcome. In most cases it is not possible to define cases into crisp sets, in such instances lower and upper approximation sets are defined. The lower approximation is defined as the collection of cases whose equivalence classes are fully contained in the 