Mining Statistically Significant Temporal Associations In Multiple Event Sequences

  • Author / Creator
    Liang, Han
  • We propose a two-phase method, called Multivariate Association Discovery (MAD), to mine temporal associations in multiple event sequences. It is assumed that a set of event sequences has been collected from an application, where each event has an id and an occurrence time. The goal is to detect temporal associations of events whose frequencies in the data are statistically significant. The motivation of our work is the observation that in practice many associated events in multiple temporal sequences do not occur concurrently but sequentially. In an empirical study, we apply MAD to tackle two problems originating from different application domains. The experimental results show that our method performed better than other related methods in these domains.

  • Subjects / Keywords
  • Graduation date
  • Type of Item
  • Degree
    Master of Science
  • DOI
  • License
    This thesis is made available by the University of Alberta Libraries with permission of the copyright owner solely for non-commercial purposes. This thesis, or any portion thereof, may not otherwise be copied or reproduced without the written consent of the copyright owner, except to the extent permitted by Canadian copyright law.
  • Language
  • Institution
    University of Alberta
  • Degree level
  • Department
    • Department of Computing Science
  • Supervisor / co-supervisor and their department(s)
    • Sander, Joerg (Computing Science)
  • Examining committee members and their departments
    • Rafiei, Davood (Computing Science)
    • Sander, Joerg (Computing Science)
    • Hurd, Pete (Psychology)
    • Nascimento, Mario A. (Computing Science)