Indirect associations between multiple items and a mining algorithm

Indirect association is a high level relationship between items and frequent itemsets in data. Current research approaches on indirect association mining are limited to indirect association between itempairs, which will discover too many rules from dataset. A formal definition of indirect association between multiple items is presented, along with an algorithm, SET_NIA, for mining this kind of indirect associations based on anti-monotonicity of indirect associations and frequent itempair support matrix. While the found rules contain same information as compared to the rules found by indirect association between itempairs mining algorithms, this notion brings space-saving in storage of the rules as well as superiority for human to understand and apply the rules. Experiments conducted on two real-word datasets show that SET_NIA can effectively find fewer rules than existing algorithms which mine indirect association between itempairs, the experimental results also prove that SET_NIA has better performance than existing algorithms.