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research-article

Efficient Mining of Association Rules in Distributed Databases

Authors:

David W. Cheung,

Yongjian FuAuthors Info & Claims

IEEE Transactions on Knowledge and Data Engineering, Volume 8, Issue 6

Pages 911 - 922

https://doi.org/10.1109/69.553158

Published: 01 December 1996 Publication History

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.

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Cited By

Li QWang HYan ZMa S(2023)Efficient mining of association rules by reducing the number of passes over the databaseJournal of Computer Science and Technology10.1007/BF0295042316:2(182-188)Online publication date: 22-Mar-2023
https://dl.acm.org/doi/10.1007/BF02950423
Zhou AZhou SJin WTian Z(2023)Generalized multidimensional association rulesJournal of Computer Science and Technology10.1007/BF0294887615:4(388-392)Online publication date: 22-Mar-2023
https://dl.acm.org/doi/10.1007/BF02948876
Zehtabchi SDaneshpour NSafkhani M(2022)A new method for privacy preserving association rule mining using homomorphic encryption with a secure communication protocolWireless Networks10.1007/s11276-022-03185-529:3(1197-1212)Online publication date: 21-Nov-2022
https://dl.acm.org/doi/10.1007/s11276-022-03185-5
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Index Terms

Efficient Mining of Association Rules in Distributed Databases

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Published In

cover image IEEE Transactions on Knowledge and Data Engineering

IEEE Transactions on Knowledge and Data Engineering Volume 8, Issue 6

December 1996

159 pages

ISSN:1041-4347

Issue’s Table of Contents

Copyright © Copyright © 1996 IEEE. All Rights Reserved.

Publisher

IEEE Educational Activities Department

United States

Publication History

Published: 01 December 1996

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Cited By

Li QWang HYan ZMa S(2023)Efficient mining of association rules by reducing the number of passes over the databaseJournal of Computer Science and Technology10.1007/BF0295042316:2(182-188)Online publication date: 22-Mar-2023
https://dl.acm.org/doi/10.1007/BF02950423
Zhou AZhou SJin WTian Z(2023)Generalized multidimensional association rulesJournal of Computer Science and Technology10.1007/BF0294887615:4(388-392)Online publication date: 22-Mar-2023
https://dl.acm.org/doi/10.1007/BF02948876
Zehtabchi SDaneshpour NSafkhani M(2022)A new method for privacy preserving association rule mining using homomorphic encryption with a secure communication protocolWireless Networks10.1007/s11276-022-03185-529:3(1197-1212)Online publication date: 21-Nov-2022
https://dl.acm.org/doi/10.1007/s11276-022-03185-5
Djenouri YLin JNørvåg KRamampiaro HYu P(2021)Exploring Decomposition for Solving Pattern Mining ProblemsACM Transactions on Management Information Systems10.1145/343977112:2(1-36)Online publication date: 11-Feb-2021
https://dl.acm.org/doi/10.1145/3439771
Diop LDiop CGiacometti ASoulet A(2020)Pattern Sampling in Distributed DatabasesAdvances in Databases and Information Systems10.1007/978-3-030-54832-2_7(60-74)Online publication date: 25-Aug-2020
https://dl.acm.org/doi/10.1007/978-3-030-54832-2_7
Ma PWei KJiang C(2019)Evaluating Distributed Transactional Database SystemProceedings of the 11th International Conference on Computer Modeling and Simulation10.1145/3307363.3307364(203-207)Online publication date: 16-Jan-2019
https://dl.acm.org/doi/10.1145/3307363.3307364
Kurnaz SMahmood RBayat OAljawarneh S(2018)Methodology Preview on Predicting Students Professional Identity Using Data Mining TechniquesProceedings of the Fourth International Conference on Engineering & MIS 201810.1145/3234698.3234754(1-6)Online publication date: 19-Jun-2018
https://dl.acm.org/doi/10.1145/3234698.3234754
Ruiz MGómez-Romero JMolina-Solana MRos MMartin-Bautista M(2017)Information fusion from multiple databases using meta-association rulesInternational Journal of Approximate Reasoning10.1016/j.ijar.2016.09.00680:C(185-198)Online publication date: 1-Jan-2017
https://dl.acm.org/doi/10.1016/j.ijar.2016.09.006
Khader NLashier AYoon S(2016)Pharmacy robotic dispensing and planogram analysis using association rule mining with prescription dataExpert Systems with Applications: An International Journal10.1016/j.eswa.2016.02.04557:C(296-310)Online publication date: 15-Sep-2016
https://dl.acm.org/doi/10.1016/j.eswa.2016.02.045
Ruiz MGmez-Romero JMolina-Solana MCampaa JMartin-Bautista M(2016)Meta-association rules for mining interesting associations in multiple datasetsApplied Soft Computing10.1016/j.asoc.2016.08.01449:C(212-223)Online publication date: 1-Dec-2016
https://dl.acm.org/doi/10.1016/j.asoc.2016.08.014
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