RuleGrowth: Mining sequential rules common to several sequences by pattern-growth

Philippe Fournier-Viger*, Roger Nkambou, Vincent Shin-Mu Tseng

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

58 Scopus citations

Abstract

Mining sequential rules from large databases is an important topic in data mining fields with wide applications. Most of the relevant studies focused on finding sequential rules appearing in a single sequence of events and the mining task dealing with multiple sequences were far less explored. In this paper, we present RuleGrowth, a novel algorithm for mining sequential rules common to several sequences. Unlike other algorithms, RuleGrowth uses a pattern-growth approach for discovering sequential rules such that it can be much more efficient and scalable. We present a comparison of RuleGrowth's performance with current algorithms for three public datasets. The experimental results show that RuleGrowth clearly outperforms current algorithms for all three datasets under low support and confidence threshold and has a much better scalability.

Original languageEnglish
Title of host publication26th Annual ACM Symposium on Applied Computing, SAC 2011
Pages956-961
Number of pages6
DOIs
StatePublished - 23 Jun 2011
Event26th Annual ACM Symposium on Applied Computing, SAC 2011 - TaiChung, Taiwan
Duration: 21 Mar 201124 Mar 2011

Publication series

NameProceedings of the ACM Symposium on Applied Computing

Conference

Conference26th Annual ACM Symposium on Applied Computing, SAC 2011
CountryTaiwan
CityTaiChung
Period21/03/1124/03/11

Keywords

  • algorithm
  • pattern-growth
  • sequential rule mining

Fingerprint Dive into the research topics of 'RuleGrowth: Mining sequential rules common to several sequences by pattern-growth'. Together they form a unique fingerprint.

  • Cite this

    Fournier-Viger, P., Nkambou, R., & Tseng, V. S-M. (2011). RuleGrowth: Mining sequential rules common to several sequences by pattern-growth. In 26th Annual ACM Symposium on Applied Computing, SAC 2011 (pp. 956-961). (Proceedings of the ACM Symposium on Applied Computing). https://doi.org/10.1145/1982185.1982394