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

  • Frequent episode discovery; spike train data
  • Event sequences
  • Multivariate associations; multiveriate motifs

• Graduation date

  Spring 2013

• Type of Item

  Thesis

• Degree

  Master of Science

• DOI

  https://doi.org/10.7939/R3BK97

• 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.