Abstract
Sequential rule mining is a well-established data mining technique for binary valued data. Many variations have been proposed, most approaches use the support-confidence-lift framework. Existing approaches make assumptions concerning the definition of what a sequence is. However, this definition is application dependent. In this paper we look at sequential rule mining with respect to multi-morbidity disease prediction which entails a rethink of the definition of what a sequence is, and a consequent rethink of the operation of the support-confidence-lift framework. A novel sequential rule mining algorithm is proposed designed to address the challenge of multi-morbidity disease prediction. The SEquential RElational N-DIsease Pattern (SERENDIP) algorithm.
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References
Agarwal, R., Srikant, R.: Fast algorithms for mining association rules. In: Proceedings of 20th International Conference on VLDB, pp. 487–499 (1994)
Agrawal, R., Imieliński, T., Swami, A.A.: Mining association rules between sets of items in large databases. In: Proceedings of the 1993 ACM SIGMOD International Conference on Management of Data - SIGMOD 1993, p. 207 (2019)
Agrawal, R., Srikant, R.: Mining sequential patterns. In: Proceedings of the Eleventh International Conference on Data Engineering, pp. 3–14 (1995)
Ayres, J., Gehrke, J., Yiu, T., Flannick, J.: Sequential pattern mining using a bitmap representation. In: Proceedings of the Eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2002), pp. 429–435 (2002)
Coenen, F., Leng, P., Ahmed, S.: Data structure for association rule mining: T-trees and p-trees. IEEE Trans. Knowl. Data Eng. 16, 774–778 (2004)
Fournier-Viger, P., Faghihi, U., Nkambou, R., Nguifod, E.M.: CMRules: mining sequential rules common to several sequences. Knowl.-Based Syst. 22, 63–76 (2012)
Fournier-Viger, P., Gueniche, T., Zida, S., Tseng, V.S.: ERMiner: sequential rule mining using equivalence classes. In: Blockeel, H., van Leeuwen, M., Vinciotti, V. (eds.) IDA 2014. LNCS, vol. 8819, pp. 108–119. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-12571-8_10
Harms, S.K., Deogun, J.S.: Sequential association rule mining with time lags. J. Intell. Inf. 22, 7–225 (2004)
Head, A., Fleming, K., Kypridemos, C., Schofield, P., O’Flaherty, M.: Dynamics of multimorbidity in England between 2004 and 2019: a descriptive epidemiology study. Eur. J. Public Health 30 (2020)
Kaur, M., Kang, S.: Market basket analysis: identify the changing trends of market data using association rule mining. Procedia Comput. Sci. 85, 78–85 (2016)
Navickas, R., Petric, V.-K., Feigl, A.B., Seychell, M.: Multimorbidity: what do we know? What should we do? J. Comorbidity 6(1), 4–11 (2016)
Rudin, C., Letham, B., Salleb-Aouissi, A., Kogan, E., Madigan, D.: Sequential event prediction with association rules. Proc. Mach. Learn. Res. 19, 615–634 (2011)
Verma, N., Singh, J.: A comprehensive review from sequential association computing to hadoop-mapreduce parallel computing in a retail scenario. J. Manag. Anal. 5(4), 359–392 (2017)
Vogeli, C., et al.: Multiple chronic conditions: prevalence, health consequences, and implications for quality, care management, and costs. J. Gen. Intern. Med. 22(3), 391–395 (2007)
Wang, Y.J., Xin, Q., Coenen, F.: Hybrid rule ordering in classification association rule mining. Trans. Mach. Learn. Data Min. Pattern Recogn. 1, 1–16 (2008)
Zaki, M.J.: Spade: an efficient algorithm for mining frequent sequences. Int. J. Mach. Learn. 42(1–2), 31–60 (2001)
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Vincent-Paulraj, A., Burnside, G., Coenen, F., Pirmohamed, M., Walker, L. (2021). Sequential Association Rule Mining Revisited: A Study Directed at Relational Pattern Mining for Multi-morbidity. In: Bramer, M., Ellis, R. (eds) Artificial Intelligence XXXVIII. SGAI-AI 2021. Lecture Notes in Computer Science(), vol 13101. Springer, Cham. https://doi.org/10.1007/978-3-030-91100-3_20
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