FHM: Faster high-utility itemset mining using estimated utility co-occurrence pruning

Philippe Fournier-Viger, Cheng Wei Wu, Souleymane Zida, Vincent Shin-Mu Tseng

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

237 Scopus citations

Abstract

High utility itemset mining is a challenging task in frequent pattern mining, which has wide applications. The state-of-the-art algorithm is HUI-Miner. It adopts a vertical representation and performs a depth-first search to discover patterns and calculate their utility without performing costly database scans. Although, this approach is effective, mining high-utility itemsets remains computationally expensive because HUI-Miner has to perform a costly join operation for each pattern that is generated by its search procedure. In this paper, we address this issue by proposing a novel strategy based on the analysis of item co-occurrences to reduce the number of join operations that need to be performed. An extensive experimental study with four real-life datasets shows that the resulting algorithm named FHM (Fast High-Utility Miner) reduces the number of join operations by up to 95 % and is up to six times faster than the state-of-the-art algorithm HUI-Miner.

Original languageEnglish
Title of host publicationFoundations of Intelligent Systems - 21st International Symposium, ISMIS 2014, Proceedings
PublisherSpringer Verlag
Pages83-92
Number of pages10
ISBN (Print)9783319083254
DOIs
StatePublished - Jun 2014
Event21st International Symposium on Methodologies for Intelligent Systems, ISMIS 2014 - Roskilde, Denmark
Duration: 25 Jun 201427 Jun 2014

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume8502 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference21st International Symposium on Methodologies for Intelligent Systems, ISMIS 2014
CountryDenmark
CityRoskilde
Period25/06/1427/06/14

Keywords

  • Frequent pattern mining
  • co-occurrence pruning
  • high-utility itemset mining
  • transaction database

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