Abstract
Searching for frequent sequential patterns has been used in several domains. We note that times granularities are more or less important with regards to the application domain. In this paper we propose a frequent interval time sequences (ITS) extraction technique from discrete temporal sequences using a sliding window approach to relax time constraints. The extracted sequences offer an interesting overview of the original data by allowing a temporal leeway on the extraction process. We formalize the ITS extraction under classical time and support constraints and conduct some experiments on synthetic data for validating our proposal.
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References
Allen, J.F.: Maintaining knowledge about temporal intervals. Communications of ACMÂ 26 (1983)
Agrawal, R., Srikant, R.: Fast algorithms for mining association rules in large databases. In: Proceedings of VLDB Conference (1994)
Agrawal, R., Srikant, R.: Mining sequential patterns. In: Proceeding of ICDE Conference. IEEE Computer Society Press (1995)
Chen, Y., Jiang, J.C., Peng, W.C., Lee, S.: An efficient algorithm for mining time interval-based patterns in large database. In: Proceedings of the 19th ACM International Conference on Information and Knowledge Management (CIKM), pp. 49–58 (2010)
Fournier-Viger, P., Nkambou, R., Nguifo, E.M.: A knowledge discovery framework for learning task models from user interactions in intelligent tutoring systems. In: Proceeding of the 7th Mexican International Conference on Artificial Intelligence, pp. 765–778 (2008)
Giannotti, F., Nanni, M., Pedreschi, D., Pinelli, F.: Mining sequences with temporal annotations. In: Proceedings of the 2006 ACM Symposium on Applied Computing, SAC 2006, 593–597. ACM (2006)
Guyet, T., Quiniou, R.: Mining temporal patterns with quantitative intervals. In: Proceedings of The 4th International Workshop on Mining Complex Data. IEEE Computer Society (2008)
Guyet, T., Quiniou, R.: Extracting temporal patterns from interval-based sequences. In: Proceedings of IJCAI Conference, pp. 1306–1311 (2011)
Hirate, Y., Yamana, H.: Generalized sequential pattern mining with item intervals. JCP. Journal of Computers 1(3), 51–60 (2006)
Pei, J., Han, J., Mortazavi-Asl, B., Pinto, H., Chen, Q., Dayal, U., Hsu, M.: Prefixspan: Mining sequential patterns by prefix-projected growth. In: Proceedings of ICDE Conference, pp. 215–224 (2001)
Pham, Q., Raschia, G., Mouaddib, N., Saint-Paul, R., Benatallah, B.: Time sequence summarization to scale up chronology-dependent applications. In: Proceedings of CIKM Conference, pp. 1137–1146 (2009)
Rabatel, J., Bringay, S., Poncelet, P.: SO_MAD: SensOr Mining for Anomaly Detection in Railway Data. In: Perner, P. (ed.) ICDM 2009. LNCS, vol. 5633, pp. 191–205. Springer, Heidelberg (2009)
Srikant, R., Agrawal, R.: Mining sequential patterns: Generalizations and performance improvements. In: Proceedings of EDBT Conference, pp. 3–17 (1996)
Wu, S., Chen, Y.: Mining non-ambiguous temporal patterns for interval-based events. IEEE Trans. on Knowl. and Data Eng. 9, 742–758 (2007)
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Ben Zakour, A., Maabout, S., Mosbah, M., Sistiaga, M. (2012). Uncertainty Interval Temporal Sequences Extraction. In: Dua, S., Gangopadhyay, A., Thulasiraman, P., Straccia, U., Shepherd, M., Stein, B. (eds) Information Systems, Technology and Management. ICISTM 2012. Communications in Computer and Information Science, vol 285. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-29166-1_23
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DOI: https://doi.org/10.1007/978-3-642-29166-1_23
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