Abstract
In this paper, we introduce a sectorial episode of the form C↦r, where C is a set of events and r is an event. The sectorial episode C↦r means that every event of C is followed by an event r. Then, by formulating the support and the confidence of sectorial episodes, in this paper, we design the algorithm Sect to extract all of the sectorial episodes that are frequent and accurate from a given event sequence by traversing it just once. Finally, by applying the algorithm Sect to bacterial culture data, we extract sectorial episodes representing drug-resistant change.
This work is partially supported by Grand-in-Aid for Scientific Research 17200011 from the Ministry of Education, Culture, Sports, Science and Technology, Japan.
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References
Agrawal, R., Mannila, H., Srikant, R., Toivonen, H., Verkamo, A.I.: Fast discovery of association rules. In: Fayyed, U.M., Piatetsky-Shapiro, G., Smyth, P., Uthurusamy, R. (eds.) Advances in Knowledge Discovery and Data Mining, pp. 307–328. AAAI/MIT Press, Cambridge (1996)
Agrawal, R., Srikant, R.: Fast algorithms for mining association rules in large databases. In: Proc. 20th VLDB, pp. 487–499 (1994)
Agrawal, R., Srikant, R.: Mining sequential patterns. In: Proc. 11th ICDE, pp. 3–14 (1995)
Bettini, C., Wang, S., Jajodia, S., Lin, J.-L.: Discovering frequent event patterns with multiple granularities in time sequences. IEEE Trans. Knowledge and Data Engineering 10, 222–237 (1998)
Mannila, H., Toivonen, H., Verkamo, A.I.: Discovery of frequent episodes in event sequences. Data Mining and Knowledge Discovery 1, 259–289 (1997)
Pei, J., Han, J., Mortazavi-Asi, B., Wang, J., Pinto, H., Chen, Q., Dayal, U., Hsu, M.-C.: Mining sequential patterns by pattern-growth: The PrefixSpan approach. IEEE Trans. Knowledge and Data Engineering 16, 1–17 (2004)
Srikant, R., Agrawal, R.: Mining sequential patterns: Generalizations and performance improvements. In: Proc. 5th EDBT, pp. 3–17 (1996)
Tsumoto, S.: Guide to the bacteriological examination data set. In: Suzuki, E. (ed.) Proc. International Workshop of KDD Challenge on Real-World Data (KDD Challenge 2000), pp. 8–12 (2000)
Wang, J., Han, J.: BIDE: Efficient mining of frequent closed sequences. In: Proc. 20th ICDE (2004)
Yan, X., Han, J., Afshar, R.: CloSpan: Mining closed sequential patterns in large datasets. In: Proc. 3rd SDM (2003)
Zhang, C., Zhang, S.: Association rule mining. Springer, Heidelberg (2002)
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Katoh, T., Hirata, K., Harao, M. (2006). Mining Sectorial Episodes from Event Sequences. In: Todorovski, L., Lavrač, N., Jantke, K.P. (eds) Discovery Science. DS 2006. Lecture Notes in Computer Science(), vol 4265. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11893318_16
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DOI: https://doi.org/10.1007/11893318_16
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-46491-4
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