EMIA: A new efficient algorithm for indirect associations mining

Indirect association is a new type of infrequent pattern, which is to “indirectly” connect two rarely co-occurred items via a frequent itemset called mediator, and if appropriately utilized it can help to identify real interesting “infrequent itempairs” from databases. In this paper, we propose a new efficient algorithm, namely EMIA, for mining indirect associations. The proposed EMIA algorithm improves the deficiency of the leading algorithm HI-mine*, alleviating unnecessary data transforming processes, thus can generate all indirect associations more efficiently using less memory storage. Experiments on both synthetic and real datasets are also made to show the effectiveness of the proposed approaches.