Fast distributed algorithm for mining association rules

David W. Cheung, Jiawei Han, Vincent T. Ng, Ada W. Fu, Yongjian Fu

Research output: Contribution to conferencePaperpeer-review

Abstract

With the existence of many large transaction databases, the huge amounts of data, the high scalability of distributed systems, and the easy partition and distribution of a centralized database, it is important to investigate efficient methods for distributed mining of association rules. This study discloses some interesting relationships between locally large and globally large itemsets and proposes an interesting distributed association rule mining algorithm, FDM (Fast Distributed Mining of association rules), which generates a small number of candidate sets and substantially reduces the number of messages to be passed at mining association rules. Our performance study shows that FDM has a superior performance over the direct application of a typical sequential algorithm. Further performance enhancement leads to a few variations of the algorithm.

Original languageEnglish (US)
Pages31-42
Number of pages12
StatePublished - Dec 1 1996
Externally publishedYes
EventProceedings of the 1996 4th International Conference on Parallel and Distributed Information Systems - Miami Beach, FL, USA
Duration: Dec 18 1996Dec 20 1996

Other

OtherProceedings of the 1996 4th International Conference on Parallel and Distributed Information Systems
CityMiami Beach, FL, USA
Period12/18/9612/20/96

ASJC Scopus subject areas

  • Engineering(all)

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