An observation-weighting method for mining actionable behavioral rules
Peng Su, Lei Wang, Daniel Dajun Zeng, Yuan Liu · 2015
One of the critical challenges faced by the mainstream data mining community is to make the mined patterns or knowledge actionable. Knowledge is considered actionable if users can take direct actions based on such knowledge to their advantage. Among the most important and distinctive actionable knowledge are actionable behavioral rules that can directly and explicitly suggest specific actions to take to influence (restrain or encourage) the behavior in the users' best interest. The problem of mining such rules is a search problem in a framework of support and expected utility. The previous definition of a rule's support assumes that each instance which supports a rule has the uniform contribution to the support. However, this assumption is usually violated in practice to some extent, and thus will hinder the performance of algorithms for mining such rules. In this paper, to handle this problem, an observation-weighting model for support and corresponding mining algorithm are proposed. The experimental results strongly suggest the validity and the superiority of our approach.