发表机构
Huizhou No. 1 High School; Guangdong University of Technology; South China University of Technology（惠州市第一中学; 广东工业大学; 华南理工大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出WAVE方法，通过对齐时间序列与波形图进行视觉-时间表征学习，实现可追溯的聚类，在10个数据集上取得最佳性能。
AI 中文摘要
多元时间序列（MTS）聚类是时间数据挖掘中的重要工具，旨在无需监督地从复杂观测中发现潜在的群体结构。尽管现有的深度聚类方法能够学习具有判别性的时间表征，但由此产生的潜在聚类往往难以与从业者可直接检查和比较的波形特征相关联，限制了其评估所发现模式是否反映有意义时间行为的能力。因此，本文提出了WAVE（波形对齐的视觉-时间嵌入），将时间序列及其确定性渲染的波形图视为同一观测的互补视图。为了产生其聚类结构可追溯到可观测波形特征的判别性表征，WAVE对齐并整合了细粒度的时间变化与整体的视觉模式，同时将每个发现的聚类与其质心最近的真实样本相关联。据此，本工作中的可解释性特指波形级别的可追溯性，而非对模型决策的一般性解释。在10个真实世界公开数据集上的广泛评估表明，WAVE在比较方法中取得了最高的宏平均聚类性能和最佳的平均排名，而定性案例研究展示了如何通过真实波形记录来检查所发现的聚类。源代码可在该https URL获取。
英文摘要
Multivariate Time Series (MTS) clustering is an important tool in temporal data mining, aiming to discover latent group structures from complex observations without supervision. Although existing deep clustering methods can learn discriminative temporal representations, the resulting latent clusters are often difficult to relate back to waveform characteristics that practitioners can directly inspect and compare, limiting their ability to assess whether the discovered patterns reflect meaningful temporal behaviors. This paper, therefore, proposes WAVE (Waveform Aligned Visual-temporal Embedding), which treats time series and their deterministically rendered waveform plots as complementary views of the same observations. To produce discriminative representations whose cluster structures can be traced to observable waveform characteristics, WAVE aligns and integrates fine-grained temporal variations with holistic visual patterns, while associating each discovered cluster with its centroid-nearest authentic sample. Accordingly, interpretability in this work specifically refers to waveform-level traceability rather than a general explanation of model decisions. Extensive evaluations across 10 real-world public datasets show that WAVE achieves the highest macro-averaged clustering performance and the best average rank among the compared methods, while qualitative case studies illustrate how the discovered clusters can be inspected through authentic waveform records. The source code is available at https://github.com/Zheng-Zhu1/WAVE.
CommentsAccepted by ICDM 2026
