Discovering and managing quantitative association rules
Chunyao Song, Tingjian Ge · 2013
Although association rule mining has been studied in the literature for quite a while and numerical attributes are prevalent, perhaps surprisingly, the state-of-the-art quantitative association rule mining is rather inefficient and ineffective in discovering all useful rules. In this paper, we propose a novel divide and conquer two-phase algorithm, which is guaranteed to find all good rules efficiently. We further devise an optimization technique for performance. Moreover, we discuss a few issues with managing and using the discovered quantitative association rules. We perform a comprehensive experimental study which shows that our algorithm is one to two orders of magnitude faster than the state-of-the-art one. In addition, we discover significantly more rules that are useful for prediction.