Abstract
In this work, we focus on a simple and fundamental question: How to find infrequent patterns, i.e. patterns with small support value, in a transactional database. In various practical applications such as science, medical and accident data analysis, frequent patterns usually represent obvious and expected phenomena. Really interesting information might hide in obscure rarity. Existing rare pattern mining approaches are mainly adapted from frequent itemset mining algorithms, which either suffered from the expensive candidate generation step or need to traverse all frequent patterns first. In this paper, we propose an infrequent pattern mining algorithm using a top-down and depth-first traversing strategy to avoid the two obstacles above. A negative itemset tree is employed to accelerate the mining process with its dataset compressing and fast counting ability.
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Adda, M., Wu, L., Feng, Y.: Rare itemset mining. In: ICMLA 2007, pp. 73–80. IEEE (2007)
Agrawal, R., Srikant, R., et al.: Fast algorithms for mining association rules. In: Proceedings of the 20th International Conference on Very Large Data Bases, VLDB, vol. 1215, pp. 487–499 (1994)
Agarwal, R.C., Aggarwal, C.C., Prasad, V.: A tree projection algorithm for generation of frequent item sets. J. Parallel Distrib. Comput. 61(3), 350–371 (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., Lin, J.C.W., Gomariz, A., Gueniche, T., Soltani, A., Deng, Z., Lam, H.T.: The SPMF open-source data mining library version 2. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 36–40. Springer (2016)
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: Proceedings of the 2000 ACM SIGMOD International Conference on Management of Data, SIGMOD’00, New York, NY, USA, pp. 1–12. ACM (2000). https://doi.org/10.1145/342009.335372
Hoque, N., Nath, B., Bhattacharyya, D.: An efficient approach on rare association rule mining. In: Proceedings of 7th International Conference on Bio-Inspired Computing: Theories and Applications (BIC-TA 2012), pp. 193–203. Springer (2013)
Koh, Y., Rountree, N.: Finding sporadic rules using apriori-inverse. Advances in Knowledge Discovery and Data Mining, pp. 153–168 (2005)
Koh, Y.S., Ravana, S.D.: Unsupervised rare pattern mining: a survey. ACM Trans. Knowl. Discov. Data (TKDD) 10(4), 45 (2016)
Liu, B., Hsu, W., Ma, Y.: Mining association rules with multiple minimum supports. In: Proceedings of the 5th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 337–341. ACM (1999)
Liu, B., Hsu, W., Ma, Y.: Pruning and summarizing the discovered associations. In: Proceedings of the 5th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 125–134. ACM (1999)
Lu, Y., Richter, F., Seidl, T.: Efficient infrequent itemset mining using depth-first and top-down lattice traversal. In: International Conference on Database Systems for Advanced Applications, pp. 908–915. Springer (2018)
Szathmary, L., Napoli, A., Valtchev, P.: Towards rare itemset mining. In: Tools with Artificial Intelligence 2007, ICTAI 2007. 19th IEEE International Conference on, 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. Discov. 28(3), 773–807 (2014)
Tsang, S., Koh, Y.S., Dobbie, G.: Rp-tree: rare pattern tree mining. In: Proceedings of the 13th International Conference on Data Warehousing and Knowledge Discovery, DaWaK’11, pp. 277–288. Springer, Berlin, Heidelberg (2011)
Uno, T., Kiyomi, M., Arimura, H.: LCM ver. 2: efficient mining algorithms for frequent/closed/maximal itemsets. In: Fimi, vol. 126 (2004)
Zaki, M.J.: Scalable algorithms for association mining. IEEE Trans. Knowl. Data Eng. 12(3), 372–390 (2000)
Zaki, M.J., Gouda, K.: Fast vertical mining using diffsets. In: Proceedings of the 9th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 326–335. ACM (2003)
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Lu, Y., Richter, F., Seidl, T. (2020). Efficient Infrequent Pattern Mining Using Negative Itemset Tree. In: Appice, A., Ceci, M., Loglisci, C., Manco, G., Masciari, E., Ras, Z. (eds) Complex Pattern Mining. Studies in Computational Intelligence, vol 880. Springer, Cham. https://doi.org/10.1007/978-3-030-36617-9_1
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DOI: https://doi.org/10.1007/978-3-030-36617-9_1
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