An attribute discretization algorithm based on Rough Set and information entropy

Attribute discretization is one of the key issues for the Rough Set theory. First, a method is proposed to compute an initial cut points set. The indistinguishable relation of decision tables did not change, and the number of elements in the initial cut points set was reduced. Then, the cut point information entropy was defined to measure the importance of a cut point. Finally, an attribute discretization algorithm based on the Rough Set and information entropy was proposed. The consistence of decision tables did not change, and the mixed decision table was considered, which contains continuous and discrete attributes. The experimental results show that this algorithm is effective and is competent for processing the large-scale datasets.