Association Rule Discovery Based on Formal Concept Analysis

Association rule discovery, as the kernel task of data mining, has been studied widely. However, most algorithms based on frequent item sets have to scan databases many times. This reduces the algorithms´ efficiency. Formal concept analysis is a useful tool in many fields. In this paper, an association rule mining algorithm is proposed based on the formal concept analysis. Through analysis the relationship between concepts in different levels, we can simplify the process of discovery association rules. Experiments on real dataset demonstrate the effectiveness of our methods.