Deriving strong association mining rules using a dependency criterion, the lift measure
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.
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Volume (Year): 1 (2009)
Issue (Month): 3 ()
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