There are two aspects of interestingness of rules that have been studied in data mining literature, objective and subjective measures. Objective measures are data-driven and domain-independent. Generally, they evaluate the rules based on their quality and similarity between them. Subjective measures, including unexpectedness, novelty, and actionability, are user-driven and domain-dependent. A rule is actionable if user can do an action to his/her advantage based on this rule. Action rules introduced in and investigated further in are constructed from actionable rules. To construct them, authors assume that attributes in a database are divided into two groups: stable and flexible. Flexible attributes provide a tool for making hints to a user what changes within some values of flexible attributes are needed for a given group of objects to re-classify these objects to another decision class. Ras and Gupta proposed how to construct action rules when information system is distributed with autonomous sites. Additionally, the notion of a cost and feasibility of an action rule is introduced in this paper. A heuristic strategy for constructing feasible action rules which have high confidence and possibly the lowest cost is also proposed. Interestingness of such action rules is the highest among actionable rules.
展开▼
