App. Res. of Comp. Accepted Paper
Vol. 43, 2026 No. 5

Time-frequency domain multi-augmented view contrastive clustering for time series

Wang Congyu
Du Mingjing
Jiangsu Key Laboratory of Educational Intelligent Technology, School of Artificial Intelligence and Computer Science, Jiangsu Normal University, Xuzhou Jiangsu 221000, China
DOI: 10.19734/j.issn.1001-3695.2025.08.0401

Abstract

Current deep time series clustering methods face challenges in effectively leveraging both time-domain and frequency-domain features, as well as in addressing the separation of representation learning and clustering. To overcome these limitations, this paper proposed a novel deep clustering method, Time-Frequency Domain Multi-Augmented View Time Series Contrastive Clustering (TFACC) . This approach enhances feature learning by incorporating a multi-view augmentation framework across time and frequency domains, combined with contrastive clustering. Specifically, TFACC utilizes a backbone network with triple shared weights to extend the conventional dual-view augmentation paradigm into multiple views, enabling the joint use of time- and frequency-domain augmentations to improve clustering performance. Based on the feature representations generated by the backbone network, both time-domain and frequency-domain augmented view pairs support instance-level and cluster-level contrastive learning. Together with the backbone network, this enables joint optimization in a fully unsupervised manner. Experimental results on 30 benchmark datasets demonstrate that TFACC consistently outperforms seven state-of-the-art baseline methods, validating its effectiveness and superiority.

Foundation Support

江苏高校"青蓝工程"资助项目
国家自然科学基金资助项目(62006104)
江苏师范大学研究生科研与实践创新基金资助项目(2024XKT2583)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2025.08.0401
Publish at: Application Research of Computers Accepted Paper, Vol. 43, 2026 No. 5

Publish History

[2026-01-20] Accepted Paper

Cite This Article

王从瑜, 杜明晶. 时频域多增强视图时间序列对比聚类 [J]. 计算机应用研究, 2026, 43 (5). (2026-01-20). https://doi.org/10.19734/j.issn.1001-3695.2025.08.0401. (Wang Congyu, Du Mingjing. Time-frequency domain multi-augmented view contrastive clustering for time series [J]. Application Research of Computers, 2026, 43 (5). (2026-01-20). https://doi.org/10.19734/j.issn.1001-3695.2025.08.0401. )

About the Journal

  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

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