综述十年时间序列聚类方法演进，连接经典与深度学习算法
Bridging the Gap: A Decade Review of Time-Series Clustering Methods
- 构建统一分类体系，整合传统与深度学习聚类方法
- 梳理从经典算法到神经网络的演进脉络，涵盖多领域应用
- 适合对时序数据分析和聚类感兴趣的研究者参考
时间序列作为序列数据的基本表示形式，在计算机科学、生物学、地质学、天文学及环境科学等多个领域得到广泛研究。随着传感、存储与网络技术的发展，高维时间序列数据不断涌现，给长时序下潜在结构的分析带来挑战。时间序列聚类作为一种成熟的无监督学习策略，通过将相似的时间序列分组，有助于揭示复杂数据中的隐藏模式。本文系统回顾了时间序列聚类方法在过去十年的发展，从经典方法到最新的神经网络模型。尽管以往综述聚焦于特定方法类别，本文旨在弥合传统聚类与新兴深度学习算法之间的差距，提出一个全面且统一的分类框架，突出关键进展，并为未来研究提供指导。
原文摘要 · Abstract (English)
Time series, as one of the most fundamental representations of sequential data, has been extensively studied across diverse disciplines, including computer science, biology, geology, astronomy, and environmental sciences. The advent of advanced sensing, storage, and networking technologies has resulted in high-dimensional time-series data, however, posing significant challenges for analyzing latent structures over extended temporal scales. Time-series clustering, an established unsupervised learning strategy that groups similar time series together, helps unveil hidden patterns in these complex datasets. In this survey, we trace the evolution of time-series clustering methods from classical approaches to recent advances in neural networks. While previous surveys have focused on specific methodological categories, we bridge the gap between traditional clustering methods and emerging deep learning-based algorithms, presenting a comprehensive, unified taxonomy for this research area. This survey highlights key developments and provides insights to guide future research in time-series clustering.
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