Abstract
Recently a new type of data source came into the focus of knowledge discovery from temporal data: interval sequences. In contrast to event sequences, interval sequences contain labeled events with a temporal extension. However, existing algorithms for mining patterns from interval sequences proved to be far from satisfying our needs. In brief, we missed an approach that at the same time: defines support as the number of pattern instances, allows input data that consists of more than one sequence, implements time constraints on a pattern instance, and counts multiple instances of a pattern within one interval sequence. In this paper we propose a new support definition which incorporates these properties. We also describe an algorithm that employs the new support definition and demonstrate its performance on field data from the automotive business.
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References
Agrawal, R., Srikant, R.: Fast algorithms for mining association rules. In: Proc. of the 20th Int. Conf. on Very Large Databases (VLDB 1994), pp. 487–499 (1994)
Agrawal, R., Srikant, R.: Mining sequential patterns. In: Proc. of the 11th Int. Conf. on Data Engineering (ICDE 1995), pp. 3–14 (1995)
Allen, J.F.: Maintaining knowledge about temporal intervals. Commun. ACM 26(11), 832–843 (1983)
Höppner, F., Klawonn, F.: Finding informative rules in interval sequences. Intelligent Data Analysis 6(3), 237–255 (2002)
Mannila, H., Toivonen, H., Verkamo, A.I.: Discovery of frequent episodes in event sequences. Data Mining and Knowledge Discovery 1(3), 259–289 (1997)
Papapetrou, P., Kollios, G., Sclaroff, S., Gunopulos, D.: Discovering frequent arrangements of temporal intervals. In: 5th IEEE Int. Conf. on Data Mining (2005)
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© 2006 Springer-Verlag Berlin Heidelberg
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Kempe, S., Hipp, J. (2006). Mining Sequences of Temporal Intervals. In: Fürnkranz, J., Scheffer, T., Spiliopoulou, M. (eds) Knowledge Discovery in Databases: PKDD 2006. PKDD 2006. Lecture Notes in Computer Science(), vol 4213. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11871637_57
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DOI: https://doi.org/10.1007/11871637_57
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-45374-1
Online ISBN: 978-3-540-46048-0
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