Efficient mining of association rules in distributed databases

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

doi:10.1109/69.553158

Efficient mining of association rules in distributed databases

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

Research output: Journal article publication › Journal article › Academic research › peer-review

269 Citations (Scopus)

Abstract

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 language	English
Pages (from-to)	911-922
Number of pages	12
Journal	IEEE Transactions on Knowledge and Data Engineering
Volume	8
Issue number	6
DOIs	https://doi.org/10.1109/69.553158
Publication status	Published - 1 Dec 1996

Keywords

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

More information

10.1109/69.553158

Cite this

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Efficient mining of association rules in distributed databases

Abstract

Keywords

ASJC Scopus subject areas

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