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Mining Interventions from Parallel Event Sequences

  • Ning Yang
  • Changjie Tang
  • Yue Wang
  • Rong Tang
  • Chuan Li
  • Jiaoling Zheng
  • Jun Zhu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5446)

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.

Keywords

Parallel Event Sequence Intervention 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Ning Yang
    • 1
  • Changjie Tang
    • 1
  • Yue Wang
    • 1
  • Rong Tang
    • 1
  • Chuan Li
    • 1
  • Jiaoling Zheng
    • 1
  • Jun Zhu
    • 2
  1. 1.School of ComputerSichuan UniversityChina
  2. 2.China Birth Defect Monitoring CentreSichuan UniversityChengduChina

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