Mining Weighted Frequent Itemsets with the Recency Constraint

Abstract

Weighted Frequent Itemset Mining (WFIM) has been proposed as an alternative to frequent itemset mining that considers not only the frequency of items but also their relative importance. However, an important limitation of WFIM is that it does not consider how recent the patterns are. To address this issue, we extend WFIM to consider the recency of patterns, and thus present the Recent Weighted Frequent Itemset Mining (RWFIM). A projection-based algorithm named RWFIM-P is designed to mine Recent Weighted Frequent Itemsets (RWFIs) based on a novel upper-bound downward closure property. Moreover, an improved algorithm named RWFIM-PE is also proposed, which introduces a new pruning strategy named Estimated Weight of 2-itemset Pruning (EW2P) to prune unpromising candidate of RWFIs early. An experimental evaluation against a state-of-the-art WFIM algorithm on the real-world and synthetic datasets show that the proposed algorithms are highly efficient.