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An asynchronous periodic sequential patterns mining algorithm with multiple minimum item supports

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

Abstract

Original sequential pattern mining model only considers occurrence frequentness of sequential patterns, disregards their occurrence periodicity. We propose the asynchronous periodic sequential pattern mining model to discover the sequential patterns which are not only occurring frequently, but also appearing periodically. For this mining model, we propose a pattern-growth mining algorithm to mine asynchronous periodic sequential patterns with multiple minimum item supports. This algorithm employs a dividing and rule method to mine asynchronous periodic sequential pattern recursively and depth first. Experimental results show the efficiency and stability of the algorithm.

Original languageEnglish
Title of host publicationProceedings - 2014 9th International Conference on P2P, Parallel, Grid, Cloud and Internet Computing, 3PGCIC 2014
EditorsLeonard Barolli, Jin Li, Marek R. Ogiela, Fatos Xhafa, Tomoki Yoshihisa
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages274-281
Number of pages8
ISBN (Electronic)9781479941711
DOIs
StatePublished - 27 Jan 2014
Event9th International Conference on P2P, Parallel, Grid, Cloud and Internet Computing, 3PGCIC 2014 - Guangzhou, Guangdong, China
Duration: 8 Nov 201410 Nov 2014

Publication series

NameProceedings - 2014 9th International Conference on P2P, Parallel, Grid, Cloud and Internet Computing, 3PGCIC 2014

Conference

Conference9th International Conference on P2P, Parallel, Grid, Cloud and Internet Computing, 3PGCIC 2014
Country/TerritoryChina
CityGuangzhou, Guangdong
Period8/11/1410/11/14

Keywords

  • asynchronous period
  • data data mining
  • multiple minimum item support
  • sequential pattern

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