Redundant association rules reduction techniques. (2nd April 2007)

• Record Type: Journal Article
• Authors: Ashrafi, Mafruz Zaman; Taniar, David; Smith, Kate
• Is Part Of: International journal of business intelligence and data mining. Volume 2:Number 1(2007)
• Page Start: 29
• Page End: 63
• Publication Date: 2007-04-02
• Subjects: association rule mining -- support -- support thresholds -- confidence -- interest -- mining methods -- redundant association rules -- data mining; 006.312
• Languages: English
• ISSNs: 1743-8187

Abstract

To discover hidden correlations, association rule mining methods use two important constraints known as support and confidence. However, mining methods are often unable to find the best value for these constraints: large number of rules when these thresholds are low; very few rules when these thresholds are high. In addition, regardless of these above thresholds, mining methods produce many rules that have identical meaning or, redundant rules. Indeed such redundant rules seem as a main impediment to efficient utilisation of discovered rules, and should be removed. To achieve this aim, here we present several methods that identify those rules that are redundant and eliminate them.