A novel pruning algorithm for mining long and maximum length frequent itemsets

Frequent itemset mining is today one of the most popular data mining techniques. Its application is, however, hindered by the high computational cost in many real-world datasets, especially for smaller values of support thresholds. In many cases, moreover, the large number of frequent itemsets discovered is overwhelming. In some real-world applications, it is sufficient to find a smaller subset of frequent itemsets, such as identifying the frequent itemsets with a maximum length. In this paper, we present a pruning algorithm, called LengthSort, that reduces the search space effectively and improves the efficiency of mining frequent itemsets with a maximum length. LengthSort prunes both the items and the transactions before constructing a Frequent Pattern tree structure. Our experiments on several datasets show that the proposed pruning techniques reduce the time needed to discover the frequent itemsets with a maximum length. The proposed pruning algorithm can also be applied to efficiently discover frequent itemsets that are longer than a user-specified threshold