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2016 International Conference on Big Data and Smart Computing (BigComp) (2016)
Hong Kong, China
Jan. 18, 2016 to Jan. 20, 2016
ISSN: 2375-9356
ISBN: 978-1-4673-8795-8
pp: 370-373
Seok-Joo Lee , School of Computer Science and Engineering, Korea University of Technology and Education, KOREATECH, CheonAn, Republic of Korea
Sungmin Kang , School of Computer Science and Engineering, Korea University of Technology and Education, KOREATECH, CheonAn, Republic of Korea
Hongyeon Kim , School of Computer Science and Engineering, Korea University of Technology and Education, KOREATECH, CheonAn, Republic of Korea
Jun-Ki Min , School of Computer Science and Engineering, Korea University of Technology and Education, KOREATECH, CheonAn, Republic of Korea
ABSTRACT
To represent complex structure, graph data is widely used in diverse applications. Among various techniques to extract meaningful information in graph data, graph clustering is an important task for the discovery of an underlying graph structure. However, the volume of graph data becomes large and increases fast recently as well as traditional clustering algorithms become computationally expensive as the size of data to be clustered increases. In this paper, we propose an efficient graph clustering algorithm running on Pregel which is one of prominent parallel processing models for large-scale graph data. Graph clustering technique merges the nodes into clusters such that the nodes in a cluster are strongly connected each other. To seek strongly connected nodes efficiently, we utilize Min-Hash which calculates similarity between vertices and/or clusters. In our experimental study, we demonstrate the efficiency and scalability of our parallel algorithm compared to existing algorithms.
INDEX TERMS
Clustering algorithms, Algorithm design and analysis, Silicon, Data mining, Computational modeling, Data models, Parallel processing
CITATION

Seok-Joo Lee, Sungmin Kang, Hongyeon Kim and Jun-Ki Min, "An efficient parallel graph clustering technique using Pregel," 2016 International Conference on Big Data and Smart Computing (BigComp)(BIGCOMP), Hong Kong, China, 2016, pp. 370-373.
doi:10.1109/BIGCOMP.2016.7425950
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