Mining indirect associations over data streams

Indirect association is a new type of infrequent pattern, which provides a new way for interpreting the value of infrequent patterns and can effectively reduce the number of uninteresting infrequent patterns. The concept of indirect association 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. All of the literature on indirect association mining, to our best knowledge, is confined to the traditional, relatively static database environment; no research work has been conducted on mining indirect associations over data streams. In this paper, we propose an approach, namely MIA-LM (Mining Indirect Association over a Landmark Model) algorithm, for mining indirect associations over data streams. The proposed method can not only discover indirect associations over data streams efficiently, but also guarantee the error of derived itemsets not to exceed a user-specified parameter. Experiments on real web-click stream are also made to show the effectiveness and efficiency of the proposed approach.