Mining Interventions from Parallel Event Sequences

Authors:
Ning Yang;Changjie Tang;Yue Wang;Rong Tang;Chuan Li;Jiaoling Zheng;Jun Zhu
Affiliations:
School of Computer, Sichuan University,;School of Computer, Sichuan University,;School of Computer, Sichuan University,;School of Computer, Sichuan University,;School of Computer, Sichuan University,;School of Computer, Sichuan University,;China Birth Defect Monitoring Centre, Sichuan University, Chengdu, China 610065
Venue:
APWeb/WAIM '09 Proceedings of the Joint International Conferences on Advances in Data and Web Management
Year:
2009

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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.