Mining Ordering Rules Using Rough Set Theory

Many real world problems deal with ordering of objects instead of classifying objects, although majority of research in machine learning and data mining has been focused on the latter. Examples of such problems are ordering of consumer products produced by different manufactures, ranking of universities, and so on. Typically, an overall ordering of objects is given. In this paper, we formulate the problem of mining ordering rules as finding association between orderings of attribute values and the overall ordering of objects. An example of ordering rules may state that “if the value of an object x on an attribute a is ordered ahead of the value of another object y on the same attribute, then x is ordered ahead of y”. For mining ordering rules, the notion of information tables is generalized to ordered information tables by adding order relations on attribute values, and rough set theory based algorithms are then used.