Mining Generalized Association Rules from a Different Perspective

In this paper, we introduce a mining algorithm from a different perspective for discovery of generalized association rules with multiple minimum supports. The perspective can be easily explained from the operations of union and intersection. Under the perspective, the characteristic of downward closure can be kept, such that the original Apriori algorithm can easily be extended to finding large itemsets. The proposed algorithm can thus meet the mining requirements of generating association rules under multiple minimum supports and managing taxonomic relationships among items. Experimental results show the effects of the parameters used in the proposed mining algorithm.