title:
 
Mining Frequent Synchronous Patterns with a Graded Notion of Synchrony
publication:
 
eusflat-15
ISBN:
  978-94-62520-77-6
ISSN:
  1951-6851
DOI:
  doi:10.2991/ifsa-eusflat-15.2015.189 (how to use a DOI)
author(s):
 
Salatiel Ezennaya-Gomez, Christian Borgelt
corresponding author:
 
Salatiel Ezennaya-Gomez
publication date:
 
June 2015
keywords:
 
Graded synchrony, synchronous events, frequent pattern, pattern mining.
abstract:
 
We present methods to find (significant) frequent synchronous patterns in event sequences, using a graded notion of synchrony that captures both the number of instances of a pattern as well as the precision of synchrony of its constituting events. Since transferring earlier work (using a binary notion of synchrony) poses certain problems, we opt for an efficient approximation scheme to compute the pattern support. Furthermore, we transfer methods for filtering for significant and removing induced patterns, which require adaptations. Finally, we demonstrate the effectiveness of our approach with experiments on a large number of data sets with injected synchronous patterns.
copyright:
 
© The authors.
This article is distributed under the terms of the Creative Commons Attribution License 4.0, which permits non-commercial use, distribution and reproduction in any medium, provided the original work is properly cited. See for details: https://creativecommons.org/licenses/by-nc/4.0/
full text: