Mining action rules from scratch

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Action rules provide hints to a business user what actions (i.e. changes within some values of flexible attributes) should be taken to improve the profitability of customers. That is, taking some actions to re-classify some customers from less desired decision class to the more desired one. However, in previous work, each action rule was constructed from two rules, extracted earlier, defining different profitability classes. In this paper, we make a first step towards formally introducing the problem of mining action rules from scratch and present formal definitions. In contrast to previous work, our formulation provides guarantee on verifying completeness and correctness of discovered action rules. In addition to formulating the problem from an inductive learning viewpoint, we provide theoretical analysis on the complexities of the problem and its variations. Furthermore, we present efficient algorithms for mining action rules from scratch. In an experimental study we demonstrate the usefulness of our techniques.

论文关键词:Action rule,Positive example,Negative example,Association rules,Flexible attributes,Stable attributes,Data mining,CRM

论文评审过程:Available online 10 May 2005.

论文官网地址:https://doi.org/10.1016/j.eswa.2005.04.031