In Search for Action Rules of the Lowest Cost

Abstract

There are two aspects of interestingness of rules that have been studied in data mining literature, objective and subjective measures ([2, 1, 3, 11, 12]). 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 [11], 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 ([2, 1, 3]). Action rules introduced in [7] and investigated further in [8] 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 (see [10]) 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.