Title: Deriving strong association mining rules using a dependency criterion, the lift measure

Authors: Sikha Bagui, Jiri Just, Subhash C. Bagui

Addresses: Department of Computer Science, University of West Florida, Pensacola, FL 32514, USA. ' Department of Computer Science, University of West Florida, Pensacola, FL 32514, USA. ' Department of Mathematics and Statistics, University of West Florida, Pensacola, FL 32514, USA

Abstract: Traditional association mining rule algorithms have two major drawbacks: first, there is a need to repeatedly scan the dataset and second, they generate too many association rules. In this paper, we have presented a dependency-based association mining rule algorithm, implemented using an array list structure in JAVA, that does not require more than one scan of the full dataset and generates a lot less strong association mining rules. The additional dependency criterion used was the lift measure.

Keywords: frequent pattern mining; association rule mining; strong association rules; dependency criterion; lift measure; array list structure.

DOI: 10.1504/IJDATS.2009.024297

International Journal of Data Analysis Techniques and Strategies, 2009 Vol.1 No.3, pp.297 - 312

Published online: 31 Mar 2009 *

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