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Extracting Borderline Associations

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3 Author(s)
Wei Kian Chen ; Dept. of Electr. & Comput. Eng. & Comput. Sci., Ohio Northern Univ., Ada, OH ; Baumgartner, D. ; Millikin, R.

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

Published in:

Computational Intelligence and Data Mining, 2007. CIDM 2007. IEEE Symposium on

Date of Conference:

March 1 2007-April 5 2007