document: Weighted Association Rule Mining using weighted support and significance framework We address the issues of discovering significant binary relationships in transaction datasets in a weighted setting . Traditional model of association rule mining is adapted to handle weighted 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 `` downward closure property '' in the weighted setting is solved by using an improved model of weighted 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 weighted settings as illustrated by experiments performed on simulated datasets .
doc_bio_tags: B I I I O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O B I I I O O O O O O O O B I I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O