Mining Generalized Actionable Rules Using Concept Hierarchies

A series of mining actionable rule methods have been proposed from various aspects, but the existing models do not incorporate the concept of hierarchy/taxonomy into the mining process and restrict the terms used to build actionable rules to atomic concepts. In order to resolve this problem, an integrated framework for extracting multiple-level actionable rules with ontology support is proposed so more generalized knowledge from data can be extracted. This type of generalized rules will contain not only the attribute values contained in data, but also some concepts encoded in a given taxonomy. Obtaining generalized actionable rules are a necessity since they provide a more general view of the domain. The proposed framework is based on a breadth-first top-downward model to be developed by extending the existing single-level actionable rule discovery methods. This framework can improve the quality of the extracted actionable rules in terms of their interestingness and understandability.