Efficient mining of association rules in distributed databases

David W. Cheung, Vincent To Yee Ng, Ada W. Fu, Yongjian Fu

Research output: Journal article publicationJournal articleAcademic researchpeer-review

268 Citations (Scopus)


Many sequential algorithms have been proposed for mining of association rules. However, very little work has been done in mining association rules in distributed databases. A direct application of sequential algorithms to distributed databases is not effective, because it requires a large amount of communication overhead. In this study, an efficient algorithm, DMA, is proposed. It generates a small number of candidate sets and requires only O(n) messages for support count exchange for each candidate set, where n is the number of sites in a distributed database. The algorithm has been implemented on an experimental test bed and its performance is studied. The results show that DMA has superior performance when comparing with the direct application of a popular sequential algorithm in distributed databases.
Original languageEnglish
Pages (from-to)911-922
Number of pages12
JournalIEEE Transactions on Knowledge and Data Engineering
Issue number6
Publication statusPublished - 1 Dec 1996


  • Association rule
  • Data mining
  • Distributed algorithm
  • Distributed data mining
  • Distributed database
  • Knowledge discovery
  • Partitioned database

ASJC Scopus subject areas

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


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