A fast distributed algorithm for mining association rules
DIS '96 Proceedings of the fourth international conference on on Parallel and distributed information systems
Parallel Mining of Association Rules
IEEE Transactions on Knowledge and Data Engineering
Weighted Association Rule Mining using weighted support and significance framework
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Privacy-Preserving Distributed Mining of Association Rules on Horizontally Partitioned Data
IEEE Transactions on Knowledge and Data Engineering
Mining Weighted Association Rules without Preassigned Weights
IEEE Transactions on Knowledge and Data Engineering
PARM—An Efficient Algorithm to Mine Association Rules From Spatial Data
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Mining high utility quantitative association rules
DaWaK'07 Proceedings of the 9th international conference on Data Warehousing and Knowledge Discovery
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In distributed association rule mining algorithm, one of the major and challenging hindrances is to reduce the communication overhead. Data sites are required to exchange lot of information in the data mining process which may generates massive communication overhead. In this paper we propose an association rule mining algorithm which minimizes the communication overhead among the participating data sites. Instead of transmitting all itemsets and their counts, we propose to transmit a binary vector and count of only frequently large itemsets. Message Passing Interface (MPI) technique is exploited to avoid broadcasting among data sites. Performance study shows that the proposed algorithm performs better than two other well known algorithms known as Fast Distributed Algorithm for Mining Association Rules (FDM) and Count Distribution (CD) in terms of communication overhead.