Weighted Association Rule Mining using weighted support and significance framework

Abstract

We address the issues of discovering significant binary relationships in transaction datasets in a setting. Traditional model of association rule mining is adapted to handle association rule mining problems where each item is allowed to have a weight. The goal is to steer the mining focus to those significant relationships involving items with significant weights rather than being flooded in the combinatornal explosion of insignificant relationships. We identify the challenge of using weights in the iterative process of generating large itemsets. The problem of invalidation of the closure property in the setting is solved by using an improved model of support measurements and exploiting a weighted downward closure property. A new algorithm called WARM (Weighted Association Rule Mining) is developed based on the improved model. The algorithm is both scalable and efficient in discovering significant relationships in settings as illustrated by experiments performed on simulated datasets.