TNS: Mining top-K non-redundant sequential rules

Philippe Fournier-Viger, S. Tseng

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

26 Scopus citations

Abstract

Mining sequential rules from sequence databases is an important research problem with wide applications. However, depending on the choice of the thresholds, current algorithms can become very slow and generate an extremely large amount of results or generate too few results, omitting valuable information. Moreover, a large proportion of sequential rules generated are redundant. In previous works, these two problems have been addressed separately. In this paper, we address both by proposing an algorithm for mining top-k non redundant sequential rules.

Original languageEnglish
Title of host publication28th Annual ACM Symposium on Applied Computing, SAC 2013
Pages164-166
Number of pages3
DOIs
StatePublished - 2013
Event28th Annual ACM Symposium on Applied Computing, SAC 2013 - Coimbra, Portugal
Duration: 18 Mar 201322 Mar 2013

Publication series

NameProceedings of the ACM Symposium on Applied Computing

Conference

Conference28th Annual ACM Symposium on Applied Computing, SAC 2013
Country/TerritoryPortugal
CityCoimbra
Period18/03/1322/03/13

Keywords

  • Algorithm
  • Redundancy
  • Sequential rules
  • Top-k pattern mining

Fingerprint

Dive into the research topics of 'TNS: Mining top-K non-redundant sequential rules'. Together they form a unique fingerprint.

Cite this