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Asynchronous parallel algorithm for mining association rules on a shared-memory multi-processors

Authors:

David W. Cheung,

Kan Hu,

Shaowei XiaAuthors Info & Claims

SPAA '98: Proceedings of the tenth annual ACM symposium on Parallel algorithms and architectures

Pages 279 - 288

https://doi.org/10.1145/277651.277694

Published: 01 June 1998 Publication History

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References

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Cited By

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Wu CJiang H(2021)Research on Parallelization of Frequent Itemsets Mining Algorithm2021 IEEE 6th International Conference on Cloud Computing and Big Data Analytics (ICCCBDA)10.1109/ICCCBDA51879.2021.9442547(210-215)Online publication date: 24-Apr-2021
https://doi.org/10.1109/ICCCBDA51879.2021.9442547
Malov AKholod IRodionov SNovikova E(2020)Data Mining Algorithms Parallelization in the Framework of Agent-Oriented ProgrammingInternet of Things, Smart Spaces, and Next Generation Networks and Systems10.1007/978-3-030-65726-0_13(135-147)Online publication date: 22-Dec-2020
https://doi.org/10.1007/978-3-030-65726-0_13
Malov ARodionov SShorov A(2019)Data Mining Algorithms Parallelization in Logic Programming Framework for Execution in ClusterInternet of Things, Smart Spaces, and Next Generation Networks and Systems10.1007/978-3-030-30859-9_8(91-103)Online publication date: 12-Sep-2019
https://doi.org/10.1007/978-3-030-30859-9_8
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Index Terms

Asynchronous parallel algorithm for mining association rules on a shared-memory multi-processors
1. Software and its engineering
  1. Software organization and properties
    1. Contextual software domains
      1. Operating systems
        Memory management
        Distributed memory
        Process management

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Published In

SPAA '98: Proceedings of the tenth annual ACM symposium on Parallel algorithms and architectures

June 1998

312 pages

ISBN:0897919890

DOI:10.1145/277651

Chairmen:
Gary Miller
Carnegie Mellon Univ., Pittsburgh, PA
,
Phillip B. Gibbons
Bell Labs

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 01 June 1998

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SPAA/PODC98

Sponsor:

SPAA/PODC98: 10th Annual ACM Symposium on Parallel Algorithms and Architectures/Symposium on Principles of Distributed Computing

June 28 - July 2, 1998

Puerto Vallarta, Mexico

Acceptance Rates

SPAA '98 Paper Acceptance Rate 30 of 84 submissions, 36%;

Overall Acceptance Rate 447 of 1,461 submissions, 31%

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Cited By

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Wu CJiang H(2021)Research on Parallelization of Frequent Itemsets Mining Algorithm2021 IEEE 6th International Conference on Cloud Computing and Big Data Analytics (ICCCBDA)10.1109/ICCCBDA51879.2021.9442547(210-215)Online publication date: 24-Apr-2021
https://doi.org/10.1109/ICCCBDA51879.2021.9442547
Malov AKholod IRodionov SNovikova E(2020)Data Mining Algorithms Parallelization in the Framework of Agent-Oriented ProgrammingInternet of Things, Smart Spaces, and Next Generation Networks and Systems10.1007/978-3-030-65726-0_13(135-147)Online publication date: 22-Dec-2020
https://doi.org/10.1007/978-3-030-65726-0_13
Malov ARodionov SShorov A(2019)Data Mining Algorithms Parallelization in Logic Programming Framework for Execution in ClusterInternet of Things, Smart Spaces, and Next Generation Networks and Systems10.1007/978-3-030-30859-9_8(91-103)Online publication date: 12-Sep-2019
https://doi.org/10.1007/978-3-030-30859-9_8
Luna JFournier‐Viger PVentura S(2019)Frequent itemset mining: A 25 years reviewWIREs Data Mining and Knowledge Discovery10.1002/widm.13299:6Online publication date: 16-Jul-2019
https://doi.org/10.1002/widm.1329
Rathee SKashyap A(2018)Adaptive-Miner: an efficient distributed association rule mining algorithm on SparkJournal of Big Data10.1186/s40537-018-0112-05:1Online publication date: 20-Feb-2018
https://doi.org/10.1186/s40537-018-0112-0
Kholod IKuprianov MShorov A(2015)Parallelization of data mining algorithms for multicore processors2015 4th Mediterranean Conference on Embedded Computing (MECO)10.1109/MECO.2015.7181919(262-267)Online publication date: Jul-2015
https://doi.org/10.1109/MECO.2015.7181919
Kholod IMalov ARodionov S(2015)Data Mining Algorithms Parallelizing in Functional Programming Language for Execution in ClusterInternet of Things, Smart Spaces, and Next Generation Networks and Systems10.1007/978-3-319-23126-6_13(140-151)Online publication date: 13-Aug-2015
https://doi.org/10.1007/978-3-319-23126-6_13
Schlegel BKiefer TKissinger TLehner W(2013)pcAprioriProceedings of the 25th International Conference on Scientific and Statistical Database Management10.1145/2484838.2484879(1-12)Online publication date: 29-Jul-2013
https://dl.acm.org/doi/10.1145/2484838.2484879
Upadhyaya S(2013)Parallel approaches to machine learning-A comprehensive surveyJournal of Parallel and Distributed Computing10.1016/j.jpdc.2012.11.00173:3(284-292)Online publication date: 1-Mar-2013
https://dl.acm.org/doi/10.1016/j.jpdc.2012.11.001
Junrui Yang Yashuang Yang (2010)A parallel algorithm for mining association rules2010 International Conference on Networking and Digital Society10.1109/ICNDS.2010.5479242(475-478)Online publication date: May-2010
https://doi.org/10.1109/ICNDS.2010.5479242
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