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CATS: a big network clustering algorithm based on triangle structures

Published: 03 April 2017 Publication History

Abstract

A huge amount of data, known as "big data," has been generated from various areas. A network is a popular data structure for presenting and analyzing big data. However, the conventional network analysis algorithms cannot cover the size of big data. To address this limitation, we propose in this paper a network clustering algorithm for a big data network using a parallel distributed computation model. To consider parallel computation concepts, we change the paradigm of the conventional clustering algorithm using triangle structures. We demonstrate that the proposed algorithm can cover a big data network that cannot be otherwise implemented using a conventional algorithm. Experimental results show that the proposed algorithm is faster than the conventional algorithm.

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Cited By

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  • (2018)CASS: A distributed network clustering algorithm based on structure similarity for large-scale networkPLOS ONE10.1371/journal.pone.020367013:10(e0203670)Online publication date: 10-Oct-2018

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    cover image ACM Conferences
    SAC '17: Proceedings of the Symposium on Applied Computing
    April 2017
    2004 pages
    ISBN:9781450344869
    DOI:10.1145/3019612
    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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    New York, NY, United States

    Publication History

    Published: 03 April 2017

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    Author Tags

    1. big data
    2. clustering
    3. network
    4. parallel distributed computation

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    • Research-article

    Funding Sources

    • Korea government (MSIP)

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    SAC 2017
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    SAC 2017: Symposium on Applied Computing
    April 3 - 7, 2017
    Marrakech, Morocco

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    Overall Acceptance Rate 1,650 of 6,669 submissions, 25%

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    The 40th ACM/SIGAPP Symposium on Applied Computing
    March 31 - April 4, 2025
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    • (2018)CASS: A distributed network clustering algorithm based on structure similarity for large-scale networkPLOS ONE10.1371/journal.pone.020367013:10(e0203670)Online publication date: 10-Oct-2018

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