Cross-Domain Contrastive Learning for Time Series Clustering

Most deep learning-based time series clustering models concentrate on data representation in a separate process from clustering. This leads to that clustering loss cannot guide feature extraction. Moreover, most methods solely analyze data from the temporal domain, disregarding the potential within the frequency domain. To address these challenges, we introduce a novel end-to-end Cross-Domain Contrastive learning model for time series Clustering (CDCC). Firstly, it integrates the clustering process and feature extraction using contrastive constraints at both cluster-level and instance-level. Secondly, the data is encoded simultaneously in both temporal and frequency domains, leveraging contrastive learning to enhance within-domain representation. Thirdly, cross-domain constraints are proposed to align the latent representations and category distribution across domains. With the above strategies, CDCC not only achieves end-to-end output but also effectively integrates frequency domains. Extensive experiments and visualization analysis are conducted on 40 time series datasets from UCR, demonstrating the superior performance of the proposed model.

Authors

• Furong Peng Institute of Big Data Science and Industry, Shanxi University School of Computer and Information Technology, Shanxi University
• Jiachen Luo Institute of Big Data Science and Industry, Shanxi University School of Computer and Information Technology, Shanxi University
• Xuan Lu College of Physics and Electronic Engineering, Shanxi University
• Sheng Wang School of Biomedical Engineering, Shanghai Jiao Tong University Shanghai United Imaging Intelligence Co., Ltd.
• Feijiang Li Shanxi University Key Laboratory of Evolutionary Science Intelligence ofShanxi Province

Keywords

• DMKM: Data Stream Mining
• DMKM: Data Visualization & Summarization