A fast distributed algorithm for mining association rules

With the existence of many large transaction databases, the huge amounts of data, the high scalability of distributed systems, and the easy partitioning and distribution of a centralized database, it is important to investigate efficient methods for distributed mining of association rules. The study...

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Bibliographic Details
Published in:Parallel and Distributed Information Systems, 4th International Conference pp. 31 - 42
Main Authors: Cheung, D.W., Jiawei Han, Ng, V.T., Fu, A.W., Yongjian Fu
Format: Conference Proceeding
Language:English
Japanese
Published: IEEE 1996
Subjects:
Association rules
Computer science
Councils
Data mining
Distributed algorithms
Distributed databases
Itemsets
Partitioning algorithms
Relational databases
Transaction databases
ISBN:9780818674754, 081867475X
Online Access:Get full text
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Summary:With the existence of many large transaction databases, the huge amounts of data, the high scalability of distributed systems, and the easy partitioning and distribution of a centralized database, it is important to investigate efficient methods for distributed mining of association rules. The study discloses some interesting relationships between locally large and globally large item sets 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. A 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.
ISBN:9780818674754
081867475X
DOI:10.1109/PDIS.1996.568665