[1]ZHAO Jia,LI Hui,XIAO Renbin,et al.Spatial-temporal density peaks clustering algorithm with relative density and mahalanobis distance[J].CAAI transactions on intelligent systems,2026,21(5):1335-1347.[doi:10.11992/tis.202511002]
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Spatial-temporal density peaks clustering algorithm with relative density and mahalanobis distance

CAAI transactions on intelligent systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 21 Number of periods: 2026 5 Page number: 1335-1347 Column: 学术论文—智能系统 Public date: 2026-09-05
Title:
Spatial-temporal density peaks clustering algorithm with relative density and mahalanobis distance
Author(s):
ZHAO Jia1; 2;  LI Hui1; 2;  XIAO Renbin3;  ZHI Yanli4;  HAN Longzhe1;  OU Qinghai5;  XIONG Xiaozhou6
1. School of Information Engineering, Jiangxi University of Water Resources and Electric Power, Nanchang 330099, China;
2. Jiangxi Province Engineering Research Center for Intelligent Processing and Early Warning Technology of Water Conservancy Big Data, Nanchang 330099, China;
3. Institute of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan 430074, China;
4. State Grid Jiangxi Electric Power Co., Ltd., Nanchang 330001, China;
5. Beijing Fibrlink Communications Co., Ltd., Beijing 100070, China;
6. Information and Telecommunication Branch, State Grid Jiangxi Electric Power Co. Ltd., Nanchang 330095, China
Keywords:
spatial-temporal clustering; density peak; clustering algorithm; spatial-temporal nearest neighbors; relative density; Mahalanobis distance; local density; relative distance
CLC:
TP181
DOI:
10.11992/tis.202511002
Abstract:
The spatial-temporal clustering by fast search and find of density peaks (ST-CFSFDP) fails to fully consider density differences among samples when processing spatial-temporal data, resulting in the inability of sample local density to accurately reflect density distribution. Additionally, using Euclidean distance to calculate relative distances between samples prevents the differentiation of clusters that are temporally adjacent but spatially distinct. To address these issues, a spatial-temporal density peaks clustering algorithm with relative density and Mahalanobis distance (ST-DPC-RDMD) is proposed. The ST-DPC-RDMD algorithm introduces the concept of spatial-temporal nearest neighbors into local density and redefines sample local density by incorporating the idea of relative density, thereby enhancing the influence of neighboring samples, amplifying the local density of samples in sparse clusters, and reducing the impact of inter-cluster density heterogeneity. It replaces Euclidean distance with Mahalanobis distance for spatial and temporal attributes, comprehensively analyzing covariance relationships across various dimensions to achieve a reasonable allocation of weights between spatial and temporal distances, thus accurately identifying clusters with characteristic differences. Experimental results on simulated datasets and real earthquake datasets demonstrate that the ST-DPC-RDMD algorithm exhibits significant advantages in clustering performance.
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