On Discovering Discriminative Itemsets Based on Detecting Frequent Itemsets in Succession

Discovery of Association Rules is one of the most common Data Mining techniques. Contrast data mining is a focused data mining research area for discovering interesting contrast patterns that state the significant differences between datasets, i.e., frequent itemsets in one dataset with much higher frequencies than the same itemsets in other datasets. In this paper, we present a new algorithm for discovering discriminative itemsets based on detecting items in succession. Association Rules in Succession consider proximity of transactions. We present extensive empirical results that exhibit the performance of the proposed algorithm with respect to similar ones.