Relative Measure for Mining Interesting Rules

This paper presents a measure which estimates interestingness of a rule relative to its corresponding common sense rules. Mining interesting rules is one of the important data mining tasks. Interesting rules bring novel knowledge that helps decision makers for advantageous actions. Interestingness is a relative issue. It is relative with what is known about the domain. A measure which can estimate the interestingness of a rule relative to the known knowledge is thus required. However, this estimation may not be accurate due to the incomplete or inaccurate knowledge about the domain. Even if it is possible to estimate interestingness, it is not so trivial to judge the interestingness from a huge set of mined rules manually. Therefore, an automated system is required that can exploit the common sense rules extracted from the data to estimate interestingness. Since the common sense rules extracted from the data can represent the true nature about the domain, it is possible to nd an interestingness measure that is free from user's biased belief. A measure that can estimate the interestingness of a rule with respect to the extracted common sense rules can be more acceptable to the user. In this work we try to show through the experiments, how our proposed relative measure can estimate relative interestingness in a rule considering already mined rules.