Abstract
Itemsets with relatively low support values are important since they usually suggest highly confident association rules, which are useful in applications such as recommendation systems and medical data analysis. However, most existing algorithms are mainly designed to mine frequent patterns and thus are time consuming in generating low support patterns. There are also a few algorithms focus on low support patterns but not efficient enough. Therefore, we propose here a low support closed pattern mining algorithm, utilizing top-down lattice traversing and novel closeness checking/pruning techniques. Extensive experiments show that our method is much more efficient to mine low support closed patterns than available alternatives.
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References
Adda, M., Wu, L., Feng, Y.: Rare itemset mining. In: Sixth International Conference on Machine Learning and Applications, ICMLA 2007, pp. 73–80. IEEE (2007)
Burdick, D., Calimlim, M., Gehrke, J.: Mafia: a maximal frequent itemset algorithm for transactional databases. In: Proceedings of the 17th International Conference on Data Engineering, pp. 443–452. IEEE (2001)
Fang, G., Pandey, G., Wang, W., Gupta, M., Steinbach, M., Kumar, V.: Mining low-support discriminative patterns from dense and high-dimensional data. IEEE Trans. Knowl. Data Eng. 24(2), 279–294 (2012)
Fournier-Viger, P., et al.: The SPMF open-source data mining library version 2. In: Berendt, B., et al. (eds.) ECML PKDD 2016. LNCS (LNAI), vol. 9853, pp. 36–40. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46131-1_8
Gupta, A., Mittal, A., Bhattacharya, A.: Minimally infrequent itemset mining using pattern-growth paradigm and residual trees. In: Proceedings of the 17th International Conference on Management of Data, p. 13 (2011)
Han, J., Pei, J., Yin, Y.: Mining frequent patterns without candidate generation. In: ACM Sigmod Record, vol. 29, pp. 1–12. ACM (2000)
Hoque, N., Nath, B., Bhattacharyya, D.: An efficient approach on rare association rule mining. In: Bansal, J., Singh, P., Deep, K., Pant, M., Nagar, A. (eds.) BIC-TA 2012, vol. 201, pp. 193–203. Springer, India (2013). https://doi.org/10.1007/978-81-322-1038-2_17
Kamehkhosh, I., Jannach, D., Ludewig, M.: A comparison of frequent pattern techniques and a deep learning method for session-based recommendation. In: RecTemp@ RecSys, pp. 50–56 (2017)
Koh, Y.S., Ravana, S.D.: Unsupervised rare pattern mining: a survey. ACM Trans. Knowl. Discov. Data (TKDD) 10(4), 45 (2016)
Leroy, V., Kirchgessner, M., Termier, A., Amer-Yahia, S.: TopPi: an efficient algorithm for item-centric mining. Inf. Syst. 64, 104–118 (2017)
Lu, Y., Richter, F., Seidl, T.: Efficient infrequent itemset mining using depth-first and top-down lattice traversal. In: Pei, J., Manolopoulos, Y., Sadiq, S., Li, J. (eds.) DASFAA 2018. LNCS, vol. 10827, pp. 908–915. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-91452-7_58
Lu, Y., Seidl, T.: Towards efficient closed infrequent itemset mining using bi-directional traversing. In: DSAA 2018, pp. 140–149. IEEE (2018)
Mannhardt, F., De Leoni, M., Reijers, H.A., Van Der Aalst, W.M.: Balanced multi-perspective checking of process conformance. Computing 98(4), 407–437 (2016)
Pasquier, N., Bastide, Y., Taouil, R., Lakhal, L.: Efficient mining of association rules using closed itemset lattices. Inf. Syst. 24(1), 25–46 (1999)
Smets, K., Vreeken, J.: Slim: directly mining descriptive patterns. In: Proceedings of SIAM International Conference on Data Mining, pp. 236–247. SIAM (2012)
Szathmary, L., Napoli, A., Valtchev, P.: Towards rare itemset mining. In: 19th IEEE International Conference on Tools with Artificial Intelligence, ICTAI 2007, vol. 1, pp. 305–312. IEEE (2007)
Troiano, L., Scibelli, G.: A time-efficient breadth-first level-wise lattice-traversal algorithm to discover rare itemsets. Data Min. Knowl. Disc. 28(3), 773–807 (2014)
Tsang, S., Koh, Y.S., Dobbie, G.: RP-tree: rare pattern tree mining. In: Cuzzocrea, A., Dayal, U. (eds.) DaWaK 2011. LNCS, vol. 6862, pp. 277–288. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-23544-3_21
Uno, T., Kiyomi, M., Arimura, H.: LCM ver. 2: Efficient mining algorithms for frequent/closed/maximal itemsets. In: Fimi, vol. 126 (2004)
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Lu, Y., Richter, F., Seidl, T. (2019). LSCMiner: Efficient Low Support Closed Itemsets Mining. In: Cheng, R., Mamoulis, N., Sun, Y., Huang, X. (eds) Web Information Systems Engineering – WISE 2019. WISE 2020. Lecture Notes in Computer Science(), vol 11881. Springer, Cham. https://doi.org/10.1007/978-3-030-34223-4_19
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DOI: https://doi.org/10.1007/978-3-030-34223-4_19
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