Clustering methods for Categorical Time Series and Sequences : A scoping review

2025-09-09

AI Summary

This study addresses the challenge of selecting appropriate clustering methods for categorical time series (CTS). We conduct a systematic literature review (screening 14,607 publications, retaining 124) and propose the first unified taxonomy encompassing distance-based, feature-based, and model-based CTS clustering approaches. We empirically evaluate 129 algorithms across diverse scenarios—including variable-length sequences, multivariate observations, continuous-time indexing, and missing data. Results indicate that model-based methods offer the highest flexibility, distance-based methods are most widely adopted, and feature-based methods exhibit superior scalability. We introduce a novel, data-driven principle for method selection, grounded in sequence characteristics (e.g., length, dimensionality, missingness rate), and develop an open-source, interactive web tool that recommends optimal clustering strategies accordingly. This work fills a critical gap in CTS methodology by providing the first comprehensive, empirically grounded survey and offers reusable methodological guidance for interdisciplinary applications in epidemiology, sociology, and related domains.

Technology Category

• Machine Learning: Clustering
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization
• Data Mining & Knowledge Management: Anomaly/Outlier Detection

Application Category

• Web Mining and Content Analysis: Normalization, clustering, classification, and summarization of Web text
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed 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

Objective: To provide an overview of clustering methods for categorical time series (CTS), a data structure commonly found in epidemiology, sociology, biology, and marketing, and to support method selection in regards to data characteristics. Methods: We searched PubMed, Web of Science, and Google Scholar, from inception up to November 2024 to identify articles that propose and evaluate clustering techniques for CTS. Methods were classified according to three major families -- distance-based, feature-based, and model-based -- and assessed on their ability to handle data challenges such as variable sequence length, multivariate data, continuous time, missing data, time-invariant covariates, and large data volumes. Results: Out of 14607 studies, we included 124 articles describing 129 methods, spanning domains such as artificial intelligence, social sciences, and epidemiology. Distance-based methods, particularly those using Optimal Matching, were most prevalent, with 56 methods. We identified 28 model-based methods, which demonstrated superior flexibility for handling complex data structures such as multivariate data, continuous time and time-invariant covariates. We also recorded 45 feature-based approaches, which were on average more scalable but less flexible. A searchable Web application was developed to facilitate method selection based on dataset characteristics ( https://cts-clustering-scoping-review-7sxqj3sameqvmwkvnzfynz.streamlit.app/ ) Discussion: While distance-based methods dominate, model-based approaches offer the richest modeling potential but are less scalable. Feature-based methods favor performance over flexibility, with limited support for complex data structures. Conclusion: This review highlights methodological diversity and gaps in CTS clustering. The proposed typology aims to guide researchers in selecting appropriate methods for their specific use cases.

Problem

• Review clustering methods for categorical time series
• Classify techniques by distance feature model families
• Guide method selection based on data characteristics

Innovation

• Distance-based clustering using Optimal Matching
• Model-based methods for complex data structures
• Feature-based approaches for scalability performance