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Cited by 2Advanced Clustering Algorithms ResearchTime Series Analysis and ForecastingData Mining and Machine Learning Applications

Clustering of Temporal and Visual Data: Recent Advancements

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Mudgal, Priyanka·2026·Data
DOI 10.3390/data11010007

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Abstract

Clustering plays a central role in uncovering latent structure within both temporal and visual data. It enables critical insights in various domains including healthcare, finance, surveillance, autonomous systems, and many more. With the growing volume and complexity of time-series and image-based datasets, there is an increasing demand for robust, flexible, and scalable clustering algorithms. Although these modalities differ—time-series being inherently sequential and vision data being spatial—they exhibit common challenges such as high dimensionality, noise, variability in alignment and scale, and the need for interpretable groupings. This survey presents a comprehensive review of recent advancements in clustering methods that are adaptable to both time-series and vision data. We explore a wide spectrum of approaches, including distance-based techniques (e.g., DTW, EMD), feature-based methods, model-based strategies (e.g., GMMs, HMMs), and deep learning frameworks such as autoencoders, self-supervised learning, and graph neural networks. We also survey hybrid and ensemble models, as well as semi-supervised and active clustering methods that leverage minimal supervision for improved performance. By highlighting both the shared principles and the modality-specific adaptations of clustering strategies, this work outlines current capabilities and open challenges, and suggests future directions toward unified, multimodal clustering systems.

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Authors
Mudgal, P.
Affiliations
Portland State University (US)
Corresponding author
Mudgal, Priyanka
ORCID
Mudgal, Priyanka
Venue
Data
Publisher
MDPI AG
Published
2026-01-04
Volume
11
Issue
1
Pages
7
ISSN
2306-5729 (ISSN-L)
DOI
10.3390/data11010007
OpenAlex
W7118163650
Cited by
2
References
116
Citations by year
2026: 1 · 2025: 1
FWCI
3.42
Type
journal article
Language
en
Open access
Licence
Locations
  • Data (publishedVersion, cc-by)
  • PDXScholar (Portland State University) (submittedVersion, cc-by)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Advanced Clustering Algorithms Research
  • Physical Sciences › Computer Science › Signal Processing › Time Series Analysis and Forecasting
  • Physical Sciences › Computer Science › Information Systems › Data Mining and Machine Learning Applications
Keywords
clustering, time series clustering, image clustering, temporal data, visual data, distance clustering, dynamic time warping, Wasserstein distance, Gaussian mixture model, hidden Markov models, autoencoder, self-supervised learning, graph neural networks, consensus clustering, semi-supervised clustering, active learning (machine learning), multimodal clustering, high dimensionality, interpretability

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