Mining Interesting Infrequent and Frequent Itemsets Based on MLMS Model

Authors:
Xiangjun Dong;Zhendong Niu;Donghua Zhu;Zhiyun Zheng;Qiuting Jia
Affiliations:
School of Management and Economics, Beijing Institute of Technology, Beijing, China 100081 and School of Information Science and Technology, Shandong Institute of Light Industry, Jinan, China 2503 ...;School of Computer Science and Technology, Beijing Institute of Technology, Beijing, China 100081;School of Management and Economics, Beijing Institute of Technology, Beijing, China 100081;School of Information & Engineering, Zhengzhou University, Zhengzhou, China 450052;School of Information Science and Technology, Shandong Institute of Light Industry, Jinan, China 250353
Venue:
ADMA '08 Proceedings of the 4th international conference on Advanced Data Mining and Applications
Year:
2008

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Abstract

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.