Mining Infrequent Itemsets Based on Multiple Level Minimum Supports

When we study positive and negative association rules simultaneously, infrequent itemsets become very important because there are many valued negative association rules in them. However, how to discover infrequent itemsets is still an open problem. In this paper, we propose a multiple level minimum supports (MLMS) model to constrain infrequent itemsets and frequent itemsets by giving deferent minimum supports to itemsets with deferent length. We compare the MLMS model with the existing models. We also design an algorithm Apriori_MLMS to discover simultaneously both frequent and infrequent itemsets based on MLMS model. The experimental results and comparisons show the validity of the algorithm.