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

基于信息熵的混合属性数据谱聚类算法

Entropy-based spectral clustering algorithm for mixed type data

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作者 姜智涵,朱军,周晓锋,李帅
机构 1.中国科学院沈阳自动化研究所,沈阳 110016;2.中国科学院大学,北京 100049;3.中国科学院网络化控制系统重点实验室,沈阳 110016
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文章编号 1001-3695(2019)08-003-2256-05
DOI 10.19734/j.issn.1001-3695.2018.02.0080
摘要 针对传统的聚类算法只能处理单属性的数据,不能很好地处理混合属性数据的聚类问题,以及目前大多数混合属性数据聚类算法对初始化敏感,不能处理任意形状的数据的问题,提出一种基于信息熵的混合属性数据谱聚类算法,用于处理混合类型数据。提出了一种新的相似性度量方式,利用谱聚类算法中的数值型数据构成的高斯核函数矩阵与新的基于信息熵的分类型数据构成的影响因子矩阵相结合代替了传统的相似度矩阵,新的相似度矩阵避免了数值属性与分类属性数据之间的转换和参数调整;把新的相似度矩阵运用到谱聚类算法中,以便于处理任意形状的数据,最终得出聚类结果。通过在UCI的数据集上的实验表明,该算法能有效地处理混合属性数据的聚类问题,且具有较高的稳定性以及良好的鲁棒性。
关键词 混合属性数据; 谱聚类; 高斯核函数; 影响因子
基金项目 工信部智能制造综合标准化与新模式应用项目(Y6L8283A01)
本文URL http://www.arocmag.com/article/01-2019-08-003.html
英文标题 Entropy-based spectral clustering algorithm for mixed type data
作者英文名 Jiang Zhihan, Zhu Jun, Zhou Xiaofeng, Li Shuai
机构英文名 1.Shenyang Institute of Automation,Chinese Academy of Sciences,Shenyang 110016,China;2.University of Chinese Academy of Sciences,Beijing 100049,China;3.Key Laboratory of Network Control System,Chinese Academy of Sciences,Shenyang 110016,China
英文摘要 The problem that the traditional clustering algorithm can only deal with single attribute data and cannot handle the clustering problem of mixed type data very well. Most of the clustering algorithms for mixed type data currently have the problem of initializing sensitive and cannot handle the data of arbitrary shape. This paper proposed an entropy-based spectral clustering algorithm for mixed type data to deal with mixed type data. First, it proposed a new similarity measure, it used the numerical data in the spectral clustering algorithm to constitute a Gaussian kernel function of the matrix, and used the classification data to constitute an entropy-based the influence factor of the matrix. A new similarity matrix combined these two matrices. Instead of the traditional similarity matrix, it proposed the new similarity matrix avoid feature transformation and parameter adjustment between the numerical data and the classification data. Then, it applied the new similarity matrix to the spectral clustering algorithm so as to deal with the data of arbitrary shape, and finally got the clustering result. Experiments on UCI data sets show that this algorithm can effectively deal with the clustering problem of mixed attribute data, with high stability and good robustness.
英文关键词 mixed type data; spectral clustering; Gaussian kernel function; influence factor
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收稿日期 2018/2/1
修回日期 2018/3/26
页码 2256-2260
中图分类号 TP391
文献标志码 A