Mining multiple-level association rules in large databases

Jiawei Han, Yongjian Fu

Research output: Contribution to journalArticlepeer-review


A top-down progressive deepening method is developed for efficient mining of multiple-level association rules from large transaction databases based on the Apriori principle. A group of variant algorithms is proposed based on the ways of sharing intermediate results, with the relative performance tested and analyzed. The enforcement of different interestingness measurements to find more interesting rules, and the relaxation of rule conditions for finding `level-crossing' association rules, are also investigated in the paper. Our study shows that efficient algorithms can be developed from large databases for the discovery of interesting and strong multiple-level association rules.

Original languageEnglish (US)
Pages (from-to)798-805
Number of pages8
JournalIEEE Transactions on Knowledge and Data Engineering
Issue number5
StatePublished - 1999
Externally publishedYes

ASJC Scopus subject areas

  • Information Systems
  • Computer Science Applications
  • Computational Theory and Mathematics


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