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.
| Original language | English |
|---|---|
| Title of host publication | 32nd Annual ACM Symposium on Applied Computing, SAC 2017 |
| Publisher | Association for Computing Machinery |
| Pages | 1590-1592 |
| Number of pages | 3 |
| ISBN (Electronic) | 9781450344869 |
| DOIs | |
| 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 |
Publication series
| Name | Proceedings of the ACM Symposium on Applied Computing |
|---|---|
| Volume | Part F128005 |
Other
| Other | 32nd Annual ACM Symposium on Applied Computing, SAC 2017 |
|---|---|
| Country | Morocco |
| City | Marrakesh |
| Period | 17/4/4 → 17/4/6 |
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All Science Journal Classification (ASJC) codes
- Software
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CATS : A big network clustering algorithm based on triangle structures. / Shin, Mincheol; Kim, Jeongwoo; Kim, Jungrim; Seo, Dongmin; Park, Chihyun; Yu, Seok Jong; Park, Sang Hyun.
32nd Annual ACM Symposium on Applied Computing, SAC 2017. Association for Computing Machinery, 2017. p. 1590-1592 (Proceedings of the ACM Symposium on Applied Computing; Vol. Part F128005).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
TY - GEN
T1 - CATS
T2 - A big network clustering algorithm based on triangle structures
AU - Shin, Mincheol
AU - Kim, Jeongwoo
AU - Kim, Jungrim
AU - Seo, Dongmin
AU - Park, Chihyun
AU - Yu, Seok Jong
AU - Park, Sang Hyun
PY - 2017/4/3
Y1 - 2017/4/3
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85020928477&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85020928477&partnerID=8YFLogxK
U2 - 10.1145/3019612.3019893
DO - 10.1145/3019612.3019893
M3 - Conference contribution
AN - SCOPUS:85020928477
T3 - Proceedings of the ACM Symposium on Applied Computing
SP - 1590
EP - 1592
BT - 32nd Annual ACM Symposium on Applied Computing, SAC 2017
PB - Association for Computing Machinery
ER -