Using a cosine-type measure to derive strong association mining rules
by Sikha Bagui, Jiri Just, Subhash C. Bagui, Rohan Hemasinha
International Journal of Knowledge Engineering and Data Mining (IJKEDM), Vol. 1, No. 1, 2010

Abstract: Association mining rule algorithms have two major drawbacks – the need to repeatedly scan the dataset and the generation of too many association rules. In this paper we present an algorithm that concentrates on addressing these drawbacks. We present a correlation based association mining rule algorithm, implemented using an arraylist structure in JAVA, that does not require more than one scan of the full dataset and generates far lot less strong association mining rules. The correlation criteria used is a cosine-type measure.

Online publication date: Thu, 08-Apr-2010

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