Graph-based clustering for detecting frequent patterns in event log data Conference Paper uri icon

abstract

  • 2016 IEEE. Finding frequent patterns is an important problem in data mining. We have devised a method for detecting frequent patterns in event log data. By representing events in a graph structure, we can generate clusters of frequently co-occurring events. This method is compared with basic association mining techniques and found to give a 'macro-level' overview of patterns, which is more interpretable. In addition, the resulting graph-based clustering output for frequently co-occurring event sets is substantially less than association mining, while providing similar information levels. Therefore, the results are more manageable for practical applications.

name of conference

  • 2016 IEEE International Conference on Automation Science and Engineering (CASE)

published proceedings

  • 2016 IEEE International Conference on Automation Science and Engineering (CASE)

author list (cited authors)

  • Sy, E., Jacobs, S. A., Dagnino, A., & Yu Ding.

citation count

  • 2

complete list of authors

  • Sy, Erika||Jacobs, Sam Ade||Dagnino, Aldo

publication date

  • August 2016

publisher