Abstract
This paper focuses on event prediction in an event sequence, particularly on distant event prediction. We aim at mining episode rules with a consequent temporally distant from the antecedent and with a minimal antecedent. To reach this goal, we propose an algorithm that determines the consequent of an episode rule at an early stage in the mining process, and that applies a span constraint on the antecedent and a gap constraint between the antecedent and the consequent. This algorithm has a complexity lower than that of state of the art algorithms, as it is independent of the gap between the antecedent and the consequent. In addition, the determination of the consequent at an early stage allows to filter out many non relevant rules early in the process, which results in an additional significant decrease of the running time. A new confidence measure is proposed, the temporal confidence, which evaluates the confidence of a rule in relation to the predefined gap. The temporal confidence is used to mine rules with a consequent that occurs mainly at a given distance. The algorithm is evaluated on an event sequence of social networks messages. We show that our algorithm mines minimal rules with a distant consequent, while requiring a small computation time. We also show that these rules can be used to accurately predict distant events.
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Achar, A., Sastry, P., et al.: Pattern-growth based frequent serial episode discovery. Data Knowl. Eng. 87, 91–108 (2013)
Agrawal, R., Imieliński, T., Swami, A.: Mining association rules between sets of items in large databases. In: ACM SIGMOD Record, vol. 22, pp. 207–216. ACM (1993)
Cho, C.W., Wu, Y.H., Yen, S.J., Zheng, Y., Chen, A.L.: On-line rule matching for event prediction. VLDB J. 20(3), 303–334 (2011)
Gan, M., Dai, H.: Fast mining of non-derivable episode rules in complex sequences. In: Torra, V., Narakawa, Y., Yin, J., Long, J. (eds.) MDAI 2011. LNCS, vol. 6820, pp. 67–78. Springer, Heidelberg (2011)
Huang, K.Y., Chang, C.H.: Efficient mining of frequent episodes from complex sequences. Inf. Syst. 33(1), 96–114 (2008)
Laxman, S., Sastry, P.S.: A survey of temporal data mining. Sadhana 31(2), 173–198 (2006)
Laxman, S., Sastry, P., Unnikrishnan, K.: A fast algorithm for finding frequent episodes in event streams. In: 13th ACM SIGKDD. ACM (2007)
Luo, J., Bridges, S.M.: Mining fuzzy association rules and fuzzy frequency episodes for intrusion detection. Int. J. Intell. Syst. 15(8), 687–703 (2000)
Mannila, H., Toivonen, H., Verkamo, A.I.: Discovery of frequent episodes in event sequences. Data Min. Knowl. Discov. 1(3), 259–289 (1997)
Méger, N., Rigotti, C.: Constraint-based mining of episode rules and optimal window sizes. In: Boulicaut, J.-F., Esposito, F., Giannotti, F., Pedreschi, D. (eds.) PKDD 2004. LNCS (LNAI), vol. 3202, pp. 313–324. Springer, Heidelberg (2004)
Neeraj, S., Swati, L.S.: Overview of non-redundant association rule mining. Res. J. Recent Sci. 1(2), 108–112 (2012). ISSN 2277-2502
Ng, A., Fu, A.W.: Mining frequent episodes for relating financial events and stock trends. In: Whang, K.-Y., Jeon, J., Shim, K., Srivastava, J. (eds.) PAKDD 2003. LNCS, vol. 2637, pp. 27–39. Springer, Heidelberg (2003)
Pasquier, N., Bastide, Y., Taouil, R., Lakhal, L.: Discovering frequent closed itemsets for association rules. In: Beeri, C., Bruneman, P. (eds.) ICDT 1999. LNCS, vol. 1540, pp. 398–416. Springer, Heidelberg (1998)
Rahal, I., Ren, D., Wu, W., Perrizo, W.: Mining confident minimal rules with fixed-consequents. In: 16th IEEE ICTAI 2004 (2004)
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This research is supported by Crédit Agricole S.A.
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Fahed, L., Brun, A., Boyer, A. (2015). Efficient Discovery of Episode Rules with a Minimal Antecedent and a Distant Consequent. In: Fred, A., Dietz, J., Aveiro, D., Liu, K., Filipe, J. (eds) Knowledge Discovery, Knowledge Engineering and Knowledge Management. IC3K 2014. Communications in Computer and Information Science, vol 553. Springer, Cham. https://doi.org/10.1007/978-3-319-25840-9_1
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