Efficient vertical mining of high utility quantitative itemsets

Chia Hua Li, Cheng Wei Wu, Vincent S. Tseng

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

3 Scopus citations

Abstract

High utility quantitative itemset mining refers to discovering sets of items that carry not only high utilities (e.g., high profits) but also quantitative attributes. Although this topic is very important to many applications, it has not been deeply explored and existing algorithms for mining high utility quantitative itemsets remain computationally expensive. To address this problem, we propose a novel algorithm named VHUQI (Vertical mining of High Utility Quantitative Itemsets) for efficiently mining high utility quantitative itemsets in databases. VHUQI adopts a vertical representation to maintain the utility information of itemsets in databases with several effective strategies integrated to prune the search space. The experimental results on both real and synthetic datasets show that VHUQI outperforms the state-of-the-art algorithms substantially in terms of both execution time and memory consumption.

Original languageEnglish
Title of host publicationProceedings - 2014 IEEE International Conference on Granular Computing, GrC 2014
EditorsYasuo Kudo, Shusaku Tsumoto
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages155-160
Number of pages6
ISBN (Electronic)9781479954643
DOIs
StatePublished - 11 Dec 2014
Event2014 IEEE International Conference on Granular Computing, GrC 2014 - Hokkaido, Japan
Duration: 22 Oct 201424 Oct 2014

Publication series

NameProceedings - 2014 IEEE International Conference on Granular Computing, GrC 2014

Conference

Conference2014 IEEE International Conference on Granular Computing, GrC 2014
CountryJapan
CityHokkaido
Period22/10/1424/10/14

Keywords

  • Quantitative itemset mining
  • high utility itemset mining
  • high utility quantitative itemset mining
  • utility mining

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