Redundant association rules reduction techniques

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.