Strongest association rules mining for personalized recommendation

Jie Li, Yong Xu, Yun Feng Wang, Chao Hsien Chu

Research output: Contribution to journalArticlepeer-review

16 Scopus citations


The notion of strongest association rules (SAR) was proposed, a matrix-based algorithm was developed for mining SAR set. As the subset of the whole association rule set, SAR set includes much less rules with the special suitable form for personalized recommendation without information loss. With the SAR set mining algorithm, the transaction database is only scanned for once, the matrix scale becomes smaller and smaller, so that the mining efficiency is improved. Experiments with three data sets show that the number of rules in SAR set in average is only 26.2% of the total number of whole association rules, which mitigates the explosion of association rules.

Original languageEnglish (US)
Pages (from-to)144-152
Number of pages9
JournalXitong Gongcheng Lilun yu Shijian/System Engineering Theory and Practice
Issue number8
StatePublished - Aug 2009

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Modeling and Simulation
  • Economic Geology
  • Computer Science Applications


Dive into the research topics of 'Strongest association rules mining for personalized recommendation'. Together they form a unique fingerprint.

Cite this