Abstract
Infrequent itemsets become very important when we study positive and negative association rules simultaneously because we can mine many valued negative association rules from them. In our previous work, we have proposed a 2LMS_inFS_FS model by assigning 2-level different minimum supports to every item to constrain frequent and infrequent itemsets respectively. But 2LMS_inFS_FS used basic Apriori algorithm to discover the defined itemsets, which is not efficient. This paper proposes an efficient model, 2L-XMMS (2-level XMMS) model, which is based on MMS (Multiple Minimum Supports) model, to improve the efficiency. The comparisons and the experimental results show that 2L-XMMS model are efficient to discover both infrequent and frequent itemsets simultaneously.
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Xi-Qing, H., Xiang-Jun, D., He, J., Ru-Nian, G. (2013). 2L-XMMS: An Efficient Method for Mining Infrequent Itemsets with 2-Level Multipul Minimum Supports. In: Yang, G. (eds) Proceedings of the 2012 International Conference on Communication, Electronics and Automation Engineering. Advances in Intelligent Systems and Computing, vol 181. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31698-2_37
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DOI: https://doi.org/10.1007/978-3-642-31698-2_37
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