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基于信息熵的混合属性数据谱聚类算法
Alternative TitleEntropy-based spectral clustering algorithm for mixed type data
姜智涵1,2,3; 朱军1,3; 周晓锋1,3; 李帅1,2,3
Department数字工厂研究室
Source Publication计算机应用研究
ISSN1001-3695
2018
Volume36Issue:8Pages:2256-2260
Contribution Rank1
Funding Organization工信部智能制造综合标准化与新模式应用项目(Y6L8283A01)
Keyword混合属性数据 谱聚类 高斯核函数 影响因子
Abstract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。首先,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;然后,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。
Other AbstractAiming at the problem that the traditional clustering algorithm can only deal with single attribute data and can’t 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 can’t 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 constitutes a Gaussian kernel function of the matrix, and used the classification data constitutes an entropy-based the influence factor of the matrix. A new similarity matrix combines 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.
Language中文
Document Type期刊论文
Identifierhttp://ir.sia.cn/handle/173321/21864
Collection数字工厂研究室
Corresponding Author姜智涵
Affiliation1.中国科学院沈阳自动化研究所;
2.中国科学院大学;
3.中国科学院网络化控制系统重点实验室
Recommended Citation
GB/T 7714
姜智涵,朱军,周晓锋,等. 基于信息熵的混合属性数据谱聚类算法[J]. 计算机应用研究,2018,36(8):2256-2260.
APA 姜智涵,朱军,周晓锋,&李帅.(2018).基于信息熵的混合属性数据谱聚类算法.计算机应用研究,36(8),2256-2260.
MLA 姜智涵,et al."基于信息熵的混合属性数据谱聚类算法".计算机应用研究 36.8(2018):2256-2260.
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