An efficient approach on rare association rule mining

Abstract

Traditional association mining techniques are based on support-confidence framework, which enable us to generate frequent rules based on frequent itemsets identified on a market basket dataset with reference to two user defined threshold minsup and minconf. However, the infrequent itemsets referred here as rare itemsets ignored by those techniques often carry useful information in certain real life applications. This paper presents an effective method to generate frequent as well as rare itemsets and also consequently the rules. The effectiveness of the proposed method is established over several synthetic and real life datasets. To address the limitations of support-confidence based frequent and rare itemsets generation technique, a multi-objective rule generation method also has been introduced. The method has been found to perform satisfactory over several real life datasets. © 2013 Springer.