Extracting Borderline Associations

In this paper, we present an extension of the well known algorithm for association mining, Apriori. This extended algorithm, ApriorBL, considers associations between items which occur together - focusing solely on the borderline cases. These borderline cases occur often enough to provide valuable information; however, there are currently no algorithms that target them. We discuss how the AprioriBL algorithm works and present a comparative analysis of Apriori and AprioriBL