Data analysis and mining in ordered information tables

Many real-world problems deal with ordering objects instead of classifying objects, although the majority of the research in machine learning and data mining has been focused on the latter. For the modeling of ordering problems, we generalize the notion of information tables to ordered information tables by adding order relations on attribute values. The problem of mining ordering rules is formulated as finding associations between the orderings of attribute values and the overall ordering of objects. An ordering rule may state, for example, 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, we first transform an ordered information table into binary information, and then apply any standard machine learning and data mining algorithms. As an illustration, we analyze in detail the Maclean's university ranking for the year 2000.