An improved data mining approach using predictive itemsets

In this paper, we present a mining algorithm to improve the efficiency of finding large itemsets. Based on the concept of prediction proposed in the (n, p) algorithm, our method considers the data dependency in the given transactions to predict promising and non-promising candidate itemsets. Our method estimates for each level a different support threshold that is derived from a data dependency parameter and determines whether an item should be included in a promising candidate itemset directly. In this way, we maintain the efficiency of finding large itemsets by reducing the number of scanning the input dataset and the number candidate items. Experimental results show our method has a better efficiency than the apriori and the (n, p) algorithms when the minimum support value is small.