Action Rules Discovery System DEAR_3

E-action rules, introduced in [8], represent actionability knowledge hidden in a decision system. They enhance action rules [3] and extended action rules [4], [6], [7] by assuming that data can be either symbolic or nominal. Several efficient strategies for mining e-action rules have been developed [6], [7], [5], and [8]. All of them assume that data are complete. Clearly, this constraint has to be relaxed since information about attribute values for some objects can be missing or represented as multi-values. To solve this problem, we present DEAR_3 which is an e-action rule generating algorithm. It has three major improvements in comparison to DEAR_2: handling data with missing attribute values and uncertain attribute values, and pruning outliers at its earlier stage.