Growing decision trees on support-less association rules
Ke Wang, Senqiang Zhou, Yu Hui He · 2000
this paper, we answer these questions and propose a general method for turning an arbitrary set of rules, in particular, association rules, into a classifier. In the past few years, many association rules and mining algorithms were proposed. However, the user often faces difficulties in making sense out of association rules. Indeed, no indication is given by association rules as to whether a specific but more confident rule or a general but less confident rule should be used to recommend products to new customers, and what hit rate a set of association rules will result in. Knowing such information is extremely important in a business decision making. Addressing such issues is the topic of this paper.