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An Efficient Large Graph Clustering Technique based on Min-Hash
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  • Journal title : Journal of KIISE
  • Volume 43, Issue 3,  2016, pp.380-388
  • Publisher : Korean Institute of Information Scientists and Engineers
  • DOI : 10.5626/JOK.2016.43.3.380
 Title & Authors
An Efficient Large Graph Clustering Technique based on Min-Hash
Lee, Seok-Joo; Min, Jun-Ki;
Graph clustering is widely used to analyze a graph and identify the properties of a graph by generating clusters consisting of similar vertices. Recently, large graph data is generated in diverse applications such as Social Network Services (SNS), the World Wide Web (WWW), and telephone networks. Therefore, the importance of graph clustering algorithms that process large graph data efficiently becomes increased. In this paper, we propose an effective clustering algorithm which generates clusters for large graph data efficiently. Our proposed algorithm effectively estimates similarities between clusters in graph data using Min-Hash and constructs clusters according to the computed similarities. In our experiment with real-world data sets, we demonstrate the efficiency of our proposed algorithm by comparing with existing algorithms.
graph clustering;min-hash;data mining;large graph;
 Cited by
스토리지 내 프로세싱 방식을 사용한 그래프 프로세싱의 최적화 방법,송내영;한혁;염헌영;

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