Using a cosine-type measure to derive strong association mining rules. (10th April 2010)

• Record Type: Journal Article
• Authors: Bagui, Sikha; Just, Jiri; Bagui, Subhash C.; Hemasinha, Rohan
• Is Part Of: International journal of knowledge engineering and data mining. Volume 1:Number 1(2010)
• Page Start: 69
• Page End: 83
• Publication Date: 2010-04-10
• Subjects: association rule mining -- Apriori algorithm -- strong association rules -- correlation measures -- cosine -- association mining; Knowledge representation (Information theory) -- Periodicals; Data mining -- Periodicals; 006.305
• Languages: English
• ISSNs: 1755-2087

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.