Abstract
Nowadays, Data mining becomes an important research domain, aiming to extract the interesting knowledge and pattern from the large databases. One of the most well-studied data mining tasks is association rules mining. It discovers and finds interesting relationships or correlations among items in large databases. A great number of algorithms have been proposed to generate the association rules, one of the main problems related to the discovery of these associations (that a decision maker faces) is the choice of the threshold of the minimum support because it influences directly the number and the quality of the discovered patterns. To bypass this challenge, we propose in this paper an approach to determine how to auto-adjust the minimum support threshold according to data by using a multiple minimum support. The experiments performed on benchmark datasets show a significant performance of the proposed approach.
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Zhou, S., Zhang, S., Karypis, G. (eds.): Advanced Data Mining and Applications: 8th International Conference, ADMA 2012, 15–18 December 2012, Proceedings, vol. 7713. Springer Science & Business Media, Nanjing (2012)
Agrawal, R., Srikant, R.: Fast algorithms for mining association rules. In: Proceedings of VLDB 1994 Proceedings of 20th International Conference on Very Large Data Bases, vol. 1215, pp. 487–499 (1994)
Zaki, M.J., Hsiao, C.J.: CHARM: an efficient algorithm for closed itemset mining. In: SDM 2002, Arlington, VA, pp. 457–473 (2002)
Pei, J., Han, J., Mao, R.: CLOSET: an efficient algorithm for mining frequent closed itemsets. In: Proceeding of the 2000 ACM-SIGMOD International Workshop Data Mining and Knowledge Discovery (DMKD 2000), Dallas, TX, pp. 11–20. ACM (2000)
Pei, J., Han, J., Mortazavi-Asl, B., Wang, J., Pinto, H., Chen, Q., Dayal, U., Hsu, M.-C.: Mining sequential patterns by pattern-growth: the prefixspan approach. IEEE Trans. Knowl. Data Eng. 16, 1424–1440 (2004)
Schmidt-Thieme, L.: Algorithmic features of Eclat. In: Proceedings of the IEEE ICDM Workshop on Frequent Itemset Mining Implementations (FIMI 2004), CEUR Workshop Proceedings, Brighton, UK, vol. 126 (2004)
Fournier-Viger, P., Wu, C.-W., Tseng, V. S.: Mining top-k association rules. In: Proceedings of the 25th Canadian Conference on Artificial Intelligence (AI 2012), pp. 61–73. Springer, Canada (2012)
Fournier-Viger, P., Tseng, V.S.: Mining top-k non-redundant association rules. In: Proceedings of 20th International Symposium, ISMIS 2012, LNCS, Macau, China, vol. 7661, pp. 31–40. Springer (2012)
Kuo, R.J., Chao, C.M., Chiu, Y.T.: Application of particle swarm optimization to association rule mining. In: Proceeding of Applied Soft Computing, pp. 326–336. Elsevier (2011)
Liu, B., Hsu, W., Ma, Y.: Mining association rules with multiple minimum supports. In: Knowledge Discovery and Databases, pp. 337–341 (1999)
Lee, Y.C., Hong, T.P., Lin, W.Y.: Mining association rules with multiple minimum supports using maximum constraints. Int. J. Approx. Reason. 40(1), 44–54 (2005)
Hu, Y.-H., Chen, Y.-L.: Mining association rules with multiple minimum supports: a new algorithm and a support tuning mechanism. Decis. Support Syst. 42(1), 1–24 (2006)
Bouker, S., Saidi, R., Ben Yahia, S., Mephu Nguifo, E.: Mining undominated association rules through interestingness measures. Int. J. Artif. Intell. Tools 23(04), 1460011 (2014)
Dahbi, A., Jabri, S., Ballouki, Y., Gadi, T.: A new method to select the interesting association rules with multiple criteria. Int. J. Intell. Eng. Syst. 10(5), 191–200 (2017)
UCI machine learning repository. https://archive.ics.uci.edu/ml/index.php. Accessed 10 Jan 2018
Frequent Itemset Mining Implementations Repository. http://fimi.ua.ac.be/data/. Accessed 10 Jan 2018
An Open-Source Data Mining Library. http://www.philippe-fournier-viger.com/spmf/index.php?link=datasets.php. Accessed 10 Jan 2018
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Dahbi, A., Balouki, Y., Gadi, T. (2018). Using Multiple Minimum Support to Auto-adjust the Threshold of Support in Apriori Algorithm. In: Abraham, A., Haqiq, A., Muda, A., Gandhi, N. (eds) Proceedings of the Ninth International Conference on Soft Computing and Pattern Recognition (SoCPaR 2017). SoCPaR 2017. Advances in Intelligent Systems and Computing, vol 737. Springer, Cham. https://doi.org/10.1007/978-3-319-76357-6_11
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DOI: https://doi.org/10.1007/978-3-319-76357-6_11
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