Mining frequent patterns in data using apriori and eclat: A comparison of the algorithm performance and association rule generation

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

Abstract

This paper aims to compare Apriori and Eclat algorithms for association rules mining by applying them on a real-world dataset. In addition to considering performance efficiency of the algorithms, the research takes into consideration the distribution of the support, as well as the number of rules generated by Apriori and Eclat.

Original languageEnglish
Title of host publication2019 6th International Conference on Systems and Informatics, ICSAI 2019
EditorsWanqing Wu, Lipo Wang, Chunlei Ji, Niansheng Chen, Sun Qiang, Xiaoyong Song, Xin Wang
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1478-1481
Number of pages4
ISBN (Electronic)9781728152561
DOIs
Publication statusPublished - 27 Nov 2019
Event6th International Conference on Systems and Informatics, ICSAI 2019 - Shanghai, China
Duration: 2 Nov 20194 Nov 2019

Publication series

Name2019 6th International Conference on Systems and Informatics, ICSAI 2019

Conference

Conference6th International Conference on Systems and Informatics, ICSAI 2019
CountryChina
CityShanghai
Period2/11/194/11/19

Keywords

  • Algorithm comparison
  • Apriori
  • Association rules
  • ECLAT

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