《计算机应用研究》|Application Research of Computers

基于自然最近邻相似图的谱聚类

Spectral clustering based on natural nearest neighbor similarity graph

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作者 刘友超,张曦煌
机构 江南大学 物联网工程学院,江苏 无锡 214122
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文章编号 1001-3695(2020)01-006-0030-04
DOI 10.19734/j.issn.1001-3695.2018.06.0460
摘要 传统谱聚类算法经常在处理一些结构复杂的数据集时效果不太理想,并且其相似度矩阵构造时参数的选取往往需要依靠多次实验及个人经验。在这种情况下,提出一种基于自然最近邻相似图的谱聚类(NSG-SC)算法。自然最近邻是一种新颖的最近邻概念,可以有效地避免K最近邻以及ε-最近邻方法需要人为设置参数的缺点。该算法构造相似度矩阵时依靠数据集自身的特性进行搜索,避免了参数选取不当以及离散点所带来的影响,更加真实地反映了数据集的结构关系。实验结果表明,提出的NSG-SC算法具有可行性和有效性。
关键词 谱聚类; 自然最近邻; 相似图; 相似度矩阵
基金项目 江苏省产学研合作项目(BY2015019-30)
本文URL http://www.arocmag.com/article/01-2020-01-006.html
英文标题 Spectral clustering based on natural nearest neighbor similarity graph
作者英文名 Liu Youchao, Zhang Xihuang
机构英文名 School of Internet of Things Engineering,Jiangnan University,Wuxi Jiangsu 214122,China
英文摘要 The traditional spectral clustering algorithm cannot often get correct results on complex data sets, and the choice of parameters of affinity matrix construction depends on multiple tests and personal experience. Based on the situation, this paper proposed a spectral clustering algorithm based on natural nearest neighbor similarity graph(NSG-SC). Natural nearest neighbor was a novel concept in terms of nearest neighbor, and it could avoid the disadvantages of K-nearest neighbor and ε-nearest neighbor. They usually needed set parameters artificially effectively. The algorithm constructed an affinity matrix depending on the characteristics of the data sets, and it avoided some adverse effects. It was that inappropriate choice of parameters and isolated points cause them. The algorithm could also reflect better characteristics of data. The results of experiment show that the proposed algorithm named NSG-SC has feasibility and effectiveness.
英文关键词 spectral clustering; natural nearest neighbor; similarity graph; affinity matrix
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收稿日期 2018/6/20
修回日期 2018/7/31
页码 30-33,39
中图分类号 TP301.6
文献标志码 A