Subspace Clustering of Heterogeneous-attribute Data Based on a New Distance Metric

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Subspace Clustering of Heterogeneous-attribute Data Based on a New Distance Metric

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[1]DENG Xiuqin,ZHENG Liping,ZHANG Yiqun,et al.Subspace Clustering of Heterogeneous-attribute Data Based on a New Distance Metric[J].Journal of Zhengzhou University (Engineering Science),2023,44(02):53-60.[doi:10.13705/j.issn.1671-6833.2023.02.007]

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Journal of Zhengzhou University (Engineering Science)[ISSN 1671-6833/CN 41-1339/T] Volume: 44卷 Number of periods: 2023 02 Page number: 53-60 Column: Public date: 2023-02-27

Title:: Subspace Clustering of Heterogeneous-attribute Data Based on a New Distance Metric

Author(s):: DENG Xiuqin; ZHENG Liping; ZHANG Yiqun; LIU Dongdong; School of Mathematics and Statistics, Guangdong University of Technology, Guangzhou 510520, Guangdong University of Technology, Guangdong University of Technology, Guangzhou 510006

Keywords:: heterogeneous-attribute data; ordinal attribute; distance metric; subspace clustering algorithm; dynamic weighting

CLC:: O235；TP311. 13

DOI:: 10.13705/j.issn.1671-6833.2023.02.007

Abstract:: Real datasets often contain categorical and numerical attributes, and categorical attributes can be divided into ordinal and nominal attributes. Datasets with both categorical and numerical attributes can be called heterogeneous-attribute data. To solve the problem that the existing distance metrics of heterogeneous-attribute data can not distinguish ordinal attributes in the categorical attributes resulting in missing information and poor clustering effect, a new subspace clustering algorithm based on distance metric was proposed. Firstly, this study summarized the existing progress of distance metric of heterogeneous-attribute data and the solutions to distinguish ordinal attribute. Then the distance formulas were defined for the attribute values of ordinal, nominal, and numerical attributes from the perspective of their natural characteristics. Subsequently, a dynamic weighting scheme was proposed to weight different attributes according to their contributed inter-and intra-cluster distances during clustering. Finally, the distance formula and dynamic weighting scheme were combined to form the distance metric applicable to heterogeneous-attribute data, and a subspace clustering algorithm for heterogeneous-attribute data was thus proposed. Because the algorithm unified the distance metric of heterogeneous-attribute data and could search clusters in subspace, it could achieve good clustering effect on heterogeneous-attribute data. Experimental results on 11 real data sets showed the effectiveness of the algorithm.

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Last Update: 2023-02-25