A Survey of Interestingness Measures for Association Rules
Yuejin Zhang, Lingling Zhang, Guangli Nie, Yong Shi · 2009
Association mining can generate large quantity of rules, most of which are not interesting to the user. Interestingness measures are used to find the truly interesting rules. This paper presents a review of the available literature on the various interestingness measures, which generally can be divided into two categories: objective measures based on the statistical strengths or properties of the discovered rules, and subjective measures which are derived from the userpsilas beliefs or expectations of their particular problem domain. We sum up twelve measure criteria which are concerned by many researchers and evaluate the strengths and weaknesses of the two categories of measures. At last, we pointed out that the combination of objective and subjective measures would be a possible research direction.