Efficient mining of high-utility sequential rules

Souleymane Zida, Philippe Fournier-Viger*, Cheng Wei Wu, Jerry Chun Wei Lin, Vincent S. Tseng

*Corresponding author for this work

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

26 Scopus citations


High-utility pattern mining is an important data mining task having wide applications. It consists of discovering patterns generating a high profit in databases. Recently, the task of high-utility sequential pattern mining has emerged to discover patterns generating a high profit in sequences of customer transactions. However, a well-known limitation of sequential patterns is that they do not provide a measure of the confidence or probability that they will be followed. This greatly hampers their usefulness for several real applications such as product recommendation. In this paper, we address this issue by extending the problem of sequential rule mining for utility mining. We propose a novel algorithm named HUSRM (High-Utility Sequential Rule Miner), which includes several optimizations to mine high-utility sequential rules efficiently. An extensive experimental study with four datasets shows that HUSRM is highly efficient and that its optimizations improve its execution time by up to 25 times and its memory usage by up to 50%.

Original languageEnglish
Title of host publicationMachine Learning and Data Mining in Pattern Recognition - 11th International Conference, MLDM 2015, Proceedings
EditorsPetra Perner
PublisherSpringer Verlag
Number of pages15
ISBN (Print)9783319210230
StatePublished - 1 Jan 2015
Event11th International Conference on Machine Learning and Data Mining in Pattern Recognition, MLDM 2015 - Hamburg, Germany
Duration: 20 Jul 201521 Jul 2015

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference11th International Conference on Machine Learning and Data Mining in Pattern Recognition, MLDM 2015


  • High-utility mining
  • Pattern mining
  • Sequential rules

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