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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References
Wu, X.-D., Zhang, C.-Q., Zhang, S.-C.: Efficient Mining of both Positive and Negative Association Rules. ACM Transactions on Information Systems, 381–405 (2004)
Haglin, D.J., Manning, A.M.: On Minimal Infrequent Itemset Mining. In: Proceedings of the International Conference DMIN, Las Vegas, Nevada, USA, pp. 141–147 (2007)
Dong, W.-J., Jiang, H., Chen, L., Liu, G.-L.: Incremental Updating Algorithm for Infrequent Itemsets on Weighted Condition. In: Proceedings in 2010 International Conference on Computer Design and Applications, ICCDA, vol. (1), pp. 36–39, 25–27 (May 2010)
Dong, X.-J., Zheng, Z.-Y., Niu, Z.-D., Jia, Q.-T.: Mining Infrequent Itemsets based on Multiple Level Minimum Supports. In: Proceedings of the Second International Conference on Innovative Computing, Information and Control, ICICIC 2007, pp. 528–532 (September 2007)
Dong, X., Niu, Z., Shi, X., Zhang, X.-d., Zhu, D.: Mining Both Positive and Negative Association Rules from Frequent and Infrequent Itemsets. In: Alhajj, R., Gao, H., Li, X., Li, J., Zaïane, O.R. (eds.) ADMA 2007. LNCS (LNAI), vol. 4632, pp. 122–133. Springer, Heidelberg (2007)
Dong, X., Niu, Z., Zhu, D., Zheng, Z., Jia, Q.: Mining Interesting Infrequent and Frequent Itemsets Based on MLMS Model. In: Tang, C., Ling, C.X., Zhou, X., Cercone, N.J., Li, X. (eds.) ADMA 2008. LNCS (LNAI), vol. 5139, pp. 444–451. Springer, Heidelberg (2008)
Dong, X.-J., Wang, S.-J., Song, H.-T.: 2-level Support based Approach for Mining Positive & Negative Association Rules. Computer Engineering 31(10), 16–18 (2005)
Dong, X.-J., Li, G., Wang, H.-G., Guo, Y.-B., Yang, Y.-Y.: Mining Infrequent Itemsets based on Extended MMS Model. In: ICIC 2007. CCIS, vol. 2, pp. 190–198. Springer, Heidelberg (2007)
Li, G., Wang, H.-G., Dong, X.-J., Yang, Y.-Y., Guo, Y.-B.: Infrequent Itemsets Mining Based on Two Level Multiple Supports. J. of Zheng Zhou Univ. (Nat. Sci. Ed.) 39(4), 94–97 (2007)
Liu, B., Hsu, W., Ma, Y.-M.: Mining Association Rules with Multiple Minimum Supports. In: Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, KDD 1999, San Diego, CA, USA, August 15-18, pp. 337–341 (1999)
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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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