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.
|Title of host publication||32nd Annual ACM Symposium on Applied Computing, SAC 2017|
|Publisher||Association for Computing Machinery|
|Number of pages||3|
|Publication status||Published - 2017 Apr 3|
|Event||32nd Annual ACM Symposium on Applied Computing, SAC 2017 - Marrakesh, Morocco|
Duration: 2017 Apr 4 → 2017 Apr 6
|Name||Proceedings of the ACM Symposium on Applied Computing|
|Other||32nd Annual ACM Symposium on Applied Computing, SAC 2017|
|Period||17/4/4 → 17/4/6|
Bibliographical noteFunding Information:
This work was supported by the "Development of biomedical data network analysis technology based on high performance computing for dementia researches (K-16-L03-C02-S02)" funded by Korea Institute of Science and Technology Information. This work was supported by the National Research Foundation of Korea(NRF) grant funded by the Korea government(MSIP) (NRF-2015R1A2A1A05001845).
© 2017 ACM.
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