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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...
Supervised discretization refers to the problem of transforming continuous attributes of a decision table into discredited ones. It is important for some artificial intelligence theories where nominal data are required or preferred. Instead of depending on the experience of human experts, supervised discretization algorithms learn from the data. However, the results of such algorithms may be sensitive...
In the rough set model based on database systems, the algorithms of calculating cores and reducts are very efficient and scalable in data mining applications. However, one serious drawback of these algorithms is that they are only applicable for consistent decision tables. In this paper we propose an algorithm which is able to receive consistent decision tables by initial decision tables while at...
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