Abstract
Discovering temporal patterns from sequence data has been an important task of data mining in recent years. In this paper a novel temporal pattern, Intervention, is proposed to capture the partial ordering relations in parallel event sequences. It is demonstrated that Intervention is essentially a deviation of generalized Markov property holding in parallel event sequences. A measure to evaluate the degree of such deviation, Intervention Intensity, is suggested, which has an important mathematical property, non-symmetry. As a result, an algorithm called MIPES for mining interventions is proposed. The time complexity of MIPES is of O(m 2) and is independent of data size, where m is the number of event types and is far smaller than the data size in practice. The experimental results show MIPES is applicable and scalable.
Supported by the National Natural Science Foundation of China under Grant No.600773169 and the 11th Five Years Key Programs for Sci. &Tech. Development of China under grant No. 2006BAI05A01.
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Yang, N. et al. (2009). Mining Interventions from Parallel Event Sequences. In: Li, Q., Feng, L., Pei, J., Wang, S.X., Zhou, X., Zhu, QM. (eds) Advances in Data and Web Management. APWeb WAIM 2009 2009. Lecture Notes in Computer Science, vol 5446. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-00672-2_27
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DOI: https://doi.org/10.1007/978-3-642-00672-2_27
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