Itemset mining has been an active area of research due to its successful application in various data mining scenarios including finding association rules. Though most of the past work has been on finding frequent itemsets, infrequent itemset mining has demonstrated its utility in web mining, bioinformatics and other fields. In this paper, we propose a new algorithm based on the pattern-growth paradigm to find minimally infrequent itemsets. A minimally infrequent itemset has no subset which is also infrequent. We also introduce the novel concept of residual trees. We further utilize the residual trees to mine multiple level minimum support itemsets where different thresholds are used for finding frequent itemsets for different lengths of the itemset. Finally, we analyze the behavior of our algorithm with respect to different parameters and show through experiments that it outperforms the competing ones.
@cite_5 , proposed the MLMS model for constraining the number of frequent and infrequent itemsets generated. A candidate generation-and-test based algorithm was proposed in @cite_5 . The downward closure property is absent in the MLMS model, and thus, the Apriori algorithm checks the supports of all possible occurring at least once in the transaction database, for finding the frequent itemsets. Generally, the support thresholds are chosen randomly for different length itemsets with the constraint @math . @cite_2 , extended their proposed algorithm from @cite_5 to include an interestingness parameter while mining frequent and infrequent itemsets.
@cite_5 @cite_2
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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. MLMS (Multiple Level Minimum Supports) model which uses multiple level minimum supports to discover infrequent itemsets and frequent itemsets simultaneously is proposed in our previous work. The reason to discover infrequent itemsets is that there are many valued negative association rules in them. However, some of the itemsets discovered by the MLMS model are not interesting and ought to be pruned. In one of Xindong Wu's papers [1], a pruning strategy (we call it Wu's pruning strategy here) is used to prune uninteresting itemsets. But the pruning strategy is only applied to single minimum support. In this paper, we modify the Wu's pruning strategy to adapt to the MLMS model to prune uninteresting itemsets and we call the MLMS model with the modified Wu's pruning strategy IMLMS (Interesting MLMS) model. Based on the IMLMS model, we design an algorithm to discover simultaneously both interesting frequent itemsets and interesting infrequent itemsets. The experimental results show the validity of the model.