|Table of Contents|

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

《南京理工大学学报》(自然科学版)[ISSN:1005-9830/CN:32-1397/N]

Issue:
2016年01期
Page:
117-
Research Field:
Publishing date:

Info

Title:
Large data parallel clustering algorithm based ondiscovery of maximal class in the community
Author(s):
Qian XiaodongCao Yang
School of Automation and Electrical Engineering,Lanzhou Jiaotong University,Lanzhou 730070,China
Keywords:
big data clustering complex network local key nodes core category maximal group fitness function parallel computing
PACS:
TP391
DOI:
-
Abstract:
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.

References:

[1] Gantz J,Reinsel D.2011 Digital universe study:extracting value from chaos[M].USA:IDC Go-to-Market Services,2011.
[2]Bughin J,Chui M,Manyika J.Clouds,big data and smart assets:ten tech-enabled business trends to watch[J].McKinsey Quarterly,2010,8:1-14
[3]王元卓,靳小龙,程学旗.网络大数据:现状与展望[J].计算机学报,2013,36(6):1125-1138.

Wang Yuanzhuo,Jin Xiaolong,Cheng Xueqi.Network big data:Present and future[J].Chinese Journal of Computers,2013,36(6):1125-1138.
[4]Guha S,Rastogi R,Shim K.Cure:an efficient clustering algorithm for large databases[J].Information System Journal,1998,26(1):35~58.
[5]Kantabutra S,Couch A L.Parallel k-means clustering algorithm on nows[J].Nectec Technical Journal,2000,1(6):243-247.
[6]Clauset A.Finding local community structure in networks[J].Physics Review E,2005,72:1-6.
[7]Lancichinetti A,Fortunato S,Kertesz J.Detection of the overlapping and hierarchical community structure in complex networks[J].New Journal of Physics,2009,11:1-18.
[8]Nicosia V,Mangioni G,Carchiolo V,et al.Extending the definition of modularity to directed graphs with overlapping communities[J].Journal of Statistical Mechanics:Theory and Experiment,2009,3:03024.
[9]Bonacich P.Factoring and weighting approaches to status scores and clique identification[J].J Math Sociol,1972,2:113-120
[10]张琨,沈海波,张宏,等.基于灰色关联分析的复杂网络节点重要性综合评价方法[J].南京理工大学学报,2012,36(4):579-586.
Zhang Kun,Shen Haibo,Zhang Hong,et al.Synthesis evaluation method for node importance in complex networks based on grey relational analysis[J].Journal of Nanjing University of Science and Technology,2012,36(4):579-586.
[11]王辉,赵文会,施佺,等.复杂网络中节点重要性Damage度量分析[J].南京理工大学学报,2012,36(6):926-931.
Wang Hui,Zhao Wenhui,Shi Quan,et al.Analysis on damage measure of vertex importance in complex networks[J].Journal of Nanjing University of Science and Technology,2012,36(6):926-931.

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Last Update: 2016-02-29