AN EFFICIENT ALGORITHM FORMINING HIGH UTILITY ASSOCIATION RULES FROM LATTICE
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DOI:
https://doi.org/10.15625/1813-9663/36/2/14353Keywords:
High utility itemsets, high utility itemset lattice, high utility association rulesAbstract
In business, most of companies focus on growing their profits. Besides considering profit from each product, they also focus on the relationship among products in order to support effective decision making, gain more profits and attract their customers, e.g. shelf arrangement, product displays, or product marketing, etc. Some high utility association rules have been proposed, however, they consume much memory and require long time processing. This paper proposes LHAR (Lattice-based for mining High utility Association Rules) algorithm to mine high utility association rules based on a lattice of high utility itemsets. The LHAR algorithm aims to generates high utility association rules during the process of building lattice of high utility itemsets, and thus it needs less memory and runtime
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[1] S. Zida, P. Fournier-Viger, J.-W. Lin, C.-W. Wu and V. Tseng, "(textit{EFIM: a fast and memory efficient algorithm for high-utility itemset mining})," Knowledge and Information Systems, vol. 51, no. 2, p. 595–625, 2017.
[2] J. Sahoo, A. K. Das and A. Goswami, "An efficient approach for mining association rules from high utility itemsets.," Expert Systems with Applications, vol. 42, no. 13, p. 5754–5778, 2015.
[3] T. Mai, B. Vo and L. Nguyen, "A lattice-based approach for mining high utility association rules," Information Sciences, vol. 399, pp. 81-97, 2017.
[4] H. Yao, H. J. Hamilton and C. J. Butz, "A foundational approach to mining itemset utilities from databases," in SIAM International Conference on Data Mining, 2004.
[5] Y. Liu, W. Liao and A. Choudhary, "A two-phase algorithm for fast discovery of high utility itemsets," in Proceedings of 9th Pacific-Asia Conference on Knowledge Discovery and Data Mining, 2005.
[6] R. Agrawal and R. Srikant, "Fast Algorithms for Mining Association Rules in Large Databases," in VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases, 1994.
[7] B. Le, H. Nguyen, T. Cao and B. Vo, "A Novel Algorithm for Mining High Utility Itemsets," in 1st Intelligent Information and Database Systems, 2009.
[8] B. Le, H. Nguyen and B. Vo, "An efficient strategy for mining high utility itemsets," International Journal of Intelligent Information and Database Systems, vol. 5, no. 2, pp. 164-176, 2011.
[9] M. Zaki, "Scalable algorithms for association mining," IEEE Transactions on Knowledge and Data Engineering, vol. 12, no. 3, pp. 372 - 390, 2000.
[10] V. S. Tseng, C.-W. Wu, B.-E. Shie and P. S. Yu, "UP-Growth: an efficient algorithm for high utility itemset mining," in 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2010.
[11] J. Han, J. Pei, Y. Yin and R. Mao, "Mining frequent patterns without candidate generation: A frequent pattern tree approach," Data Mining and Knowledge Discovery, vol. 8, no. 1, pp. 53-87, 2004.
[12] V. S. Tseng, B. E. Shie, C. W. Wu and P. S. Yu, "Efficient algorithms for mining high utility itemsets from transactional databases," IEEE Transactions on Knowledge and Data Engineering, vol. 25, no. 8, pp. 1772-1786, 2013.
[13] U. Yun, H. Ryang and K. H. Ryu, "High utility itemset mining with techniques for reducing overestimated utilities and pruning candidates," Expert Systems with Applications, vol. 41, no. 8, pp. 3861-3878, 2014.
[14] P. Fournier-Viger, C.-W. Wu, S. Zida and V. Tseng, "FHM: Faster High-Utility Itemset Mining Using Estimated Utility Co-occurrence Pruning," in 21st International Symposium on Methodologies of Intelligent Systems, 2014.
[15] Y. Liu, W. K. Liao and A. N. Choudhary, "A two-phase algorithm for fast discovery of high utility itemsets," in Pacific-Asia Conf. Knowledge Discovery and Data Mining, 2005.
[16] M. Liu and J. Qu, "High utility itemsets without candidate generation," in 21st ACM International Conference on Information and Knowledge Management, 2012.
[17] S. Krishnamoorthy, "HMiner: Efficiently Mining High Utility Itemsets," Expert Systems with Applications, vol. 90, no. C, pp. 168-183, 2017.
[18] L. Nguyen, P. Nguyen, T. Nguyen, B. Vo, P. Fournier-Viger and V. Tseng, "Mining high-utility itemsets in dynamic profit databases," Knowledge-Based Systems, vol. 175, pp. 130-144, 2019.
[19] P. Fournier-Viger, A. Gomariz, T. Gueniche, A. Soltani, C.-W. Wu and V. S. Tseng, "SPMF: a Java open-source pattern mining library," The Journal of Machine Learning Research, vol. 15, no. 1, pp. 3389-3393, 2014.
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