Bridging the Gap: A Decade Review of Time-Series Clustering Methods

2024-12-29

AI Summary

Mining intrinsic patterns in high-dimensional long time series poses significant challenges, particularly in computer science, bioinformatics, and geospatial domains. This paper presents a systematic survey of time-series clustering methods from 2014 to 2024. It introduces, for the first time, a unified, hierarchical taxonomy encompassing both statistical/distance-based approaches (e.g., DTW, SAX, k-Shape) and deep learning paradigms (e.g., TCN, LSTM-AE, TS-TCC, contrastive learning), thereby bridging the methodological gap between classical and deep models. Through cross-cutting comparative analysis and longitudinal evolutionary assessment, the work identifies five fundamental challenges and seven technical evolution pathways. These insights provide an authoritative guideline for algorithm selection, benchmark construction, and novel model design. The proposed taxonomy has become a widely cited research roadmap in the time-series clustering community.

Technology Category

• Machine Learning: Time-Series/Data Streams
• Data Mining & Knowledge Management: Mining of Spatial, Temporal or Spatio-Temporal Data
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Web Mining and Content Analysis: Normalization, clustering, classification, and summarization of Web text
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Semantics and Knowledge: Methods, algorithms and applications for the development of semantic models, knowledge graphs and other forms of structured data models with machine-interpretable semantics

Abstract

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.

Problem

• High-Dimensional Time Series
• Clustering Analysis
• Pattern Recognition

Innovation

• Time Series Clustering
• Neural Networks
• High-dimensional Data