Abstract
Episode rule mining is a popular data mining task for analyzing a sequence of events or symbols. It consists of identifying subsequences of events that frequently appear in a sequence and then to combine them to obtain episode rules that reveal strong relationships between events. But a key problem is that each rule requires a strict ordering of events. As a result, similar rules are treated differently, though they in practice often describe a same situation. To find a smaller set of rules that are more general and can replace numerous episode rules, this paper introduces a novel type of rules called partially-ordered episode rules, where events in a rule are partially ordered. To efficiently find all these rules in a sequence, an efficient algorithm named POERM (Partially-Ordered Episode Rule Miner) is presented. An experimental evaluation on several benchmark dataset shows that POERM has excellent performance.
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References
Ao, X., Luo, P., Li, C., Zhuang, F., He, Q.: Online frequent episode mining. In: 2015 IEEE 31st International Conference on Data Engineering, pp. 891–902. IEEE (2015)
Ao, X., Luo, P., Wang, J., Zhuang, F., He, Q.: Mining precise-positioning episode rules from event sequences. IEEE Trans. Knowl. Data Eng. 30(3), 530–543 (2017)
Ao, X., Shi, H., Wang, J., Zuo, L., Li, H., He, Q.: Large-scale frequent episode mining from complex event sequences with hierarchies. ACM Trans. Intell. Syst. Technol (TIST) 10(4), 1–26 (2019)
Fahed, L., Brun, A., Boyer, A.: Deer: distant and essential episode rules for early prediction. Exp. Syst. Appl. 93, 283–298 (2018)
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
Fournier-Viger, P., Lin, J.C.W., Kiran, U.R., Koh, Y.S.: A survey of sequential pattern mining. Data Sci. Pattern Recogn. 1(1), 54–77 (2017)
Fournier-Viger, P., Wu, C.W., Tseng, V.S., Cao, L., Nkambou, R.: Mining partially-ordered sequential rules common to multiple sequences. IEEE Trans. Knowl. Data Eng. 27(8), 2203–2216 (2015)
Fournier-Viger, P., Yang, Y., Yang, P., Lin, J.C.-W., Yun, U.: TKE: mining top-k frequent episodes. In: Fujita, H., Fournier-Viger, P., Ali, M., Sasaki, J. (eds.) IEA/AIE 2020. LNCS (LNAI), vol. 12144, pp. 832–845. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-55789-8_71
Huang, K., Chang, C.: Efficient mining of frequent episodes from complex sequences. Inf. Syst. 33(1), 96–114 (2008)
Koh, Y.S., Ravana, S.D.: Unsupervised rare pattern mining: a survey. ACM Trans. Knowl. Disc. Data (TKDD) 10(4), 1–29 (2016)
Lin, Y.F., Huang, C.F., Tseng, V.S.: A novel methodology for stock investment using high utility episode mining and genetic algorithm. Appl. Soft Comput. 59, 303–315 (2017)
Mannila, H., Toivonen, H., Verkamo, A.I.: Discovering frequent episodes in sequences. In: Proceedings of 1st International Conference on Knowledge Discovery and Data Mining (1995)
Song, W., Huang, C.: Mining high average-utility itemsets based on particle swarm optimization. Data Sci. Pattern Recogn. 4(2), 19–32 (2020)
Su, M.Y.: Applying episode mining and pruning to identify malicious online attacks. Comput. Electr. Eng. 59, 180–188 (2017)
Truong, T., Duong, H., Le, B., Fournier-Viger, P.: Fmaxclohusm: an efficient algorithm for mining frequent closed and maximal high utility sequences. Eng. Appl. Artif. Intell 85, 1–20 (2019)
Wenzhe, L., Qian, W., Luqun, Y., Jiadong, R., Davis, D.N., Changzhen, H.: Mining frequent intra-sequence and inter-sequence patterns using bitmap with a maximal span. In: Proceedings of 14th Web Information Systems and Applications Conference, pp. 56–61. IEEE (2017)
You, T., Li, Y., Sun, B., Du, C.: Multi-source data stream online frequent episode mining. IEEE Access 8, 107465–107478 (2020)
Yun, U., Nam, H., Lee, G., Yoon, E.: Efficient approach for incremental high utility pattern mining with indexed list structure. Fut. Gener. Comput. Syst 95, 221–239 (2019)
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Fournier-Viger, P., Chen, Y., Nouioua, F., Lin, J.CW. (2021). Mining Partially-Ordered Episode Rules in an Event Sequence. In: Nguyen, N.T., Chittayasothorn, S., Niyato, D., Trawiński, B. (eds) Intelligent Information and Database Systems. ACIIDS 2021. Lecture Notes in Computer Science(), vol 12672. Springer, Cham. https://doi.org/10.1007/978-3-030-73280-6_1
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