Efficient mining of discriminative itemsets
Majid Seyfi, Richi Nayak, Yue Xu, Shlomo Geva · Proceedings of the International Conference on Web Intelligence · 2017
Discriminative itemsets can be more useful than frequent itemsets as the former identifies the frequent itemsets in one dataset with much higher frequencies than the same itemsets in other datasets. The discriminative itemsets can distinguish the target dataset from all others. The discriminative itemsets are a small subset of frequent itemsets. The efficient mining of discriminative itemsets is a challenging problem, since the Apriori property of frequent itemsets is not applicable, and the designed algorithms must deal with the exponential number of itemset combinations in more than one dataset. In this paper, a novel algorithm, called DISSparse, is proposed for efficient mining of discriminative itemsets. Two determinative heuristics are proposed for limiting the mining of discriminative itemsets to the potential discriminative itemsets. Our experiments show the efficient time and space usage of the proposed algorithm in the large and complex datasets.