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Large data parallel clustering algorithm based ondiscovery of maximal class in the community


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Large data parallel clustering algorithm based ondiscovery of maximal class in the community
Qian XiaodongCao Yang
School of Automation and Electrical Engineering,Lanzhou Jiaotong University,Lanzhou 730070,China
big data clustering complex network local key nodes core category maximal group fitness function parallel computing
In order to find the network structure in the big data accurately and quickly,a large data clustering algorithm based on community clustering is proposed here.The key local node and improved fitness function are introduced to reduce the time consumption caused by the initial node’s uncertainty and the fitness function computing.For the formation of the initial community,this paper introduces the conception of the maximum clique.The conclusion that the core category of the community is made up of the maximum clique is drawn through analyzing its properties.This paper proposes the way of getting a local core class through finding the maximum clique.This paper proposes a parallel strategy of the maximum clique discovery algorithm and tests it in the real data sets.The experimental results show this algorithm is feasible and effective which can be applied to finding the network structure of large-scale data.


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Last Update: 2016-02-29