# Fuzzy Cluster-Aware Contrastive Clustering for Time Series

arXiv: https://arxiv.org/abs/2503.22211 · HTML: https://mlsanity.com/paper/2503.22211/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 49 references that are in the map.
- First posted on arXiv 28 Mar 2025.

## Builds on (most cited first, 24 of 24)

- [An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling](https://mlsanity.com/paper/1803.01271/) (2018)
- [TS2Vec: Towards Universal Representation of Time Series](https://mlsanity.com/paper/2106.10466/) (2021)
- [The UCR Time Series Archive](https://mlsanity.com/paper/1810.07758/) (2018)
- [Hard Negative Mixing for Contrastive Learning](https://mlsanity.com/paper/2010.01028/) (2020)
- [Time-Series Representation Learning via Temporal and Contextual Contrasting](https://mlsanity.com/paper/2106.14112/) (2021)
- [Unsupervised Scalable Representation Learning for Multivariate Time Series](https://mlsanity.com/paper/1901.10738/) (2019)
- [Contrastive Clustering](https://mlsanity.com/paper/2009.09687/) (2020)
- [Unsupervised Representation Learning for Time Series with Temporal Neighborhood Coding](https://mlsanity.com/paper/2106.00750/) (2021)
- [ADAHESSIAN: An Adaptive Second Order Optimizer for Machine Learning](https://mlsanity.com/paper/2006.00719/) (2020)
- [Self-Supervised Learning for Time Series Analysis: Taxonomy, Progress, and Prospects](https://mlsanity.com/paper/2306.10125/) (2023)
- [Graph Contrastive Clustering](https://mlsanity.com/paper/2104.01429/) (2021)
- [Twin Contrastive Learning for Online Clustering](https://mlsanity.com/paper/2210.11680/) (2022)
- [Contrast Everything: A Hierarchical Contrastive Framework for Medical Time-Series](https://mlsanity.com/paper/2310.14017/) (2023)
- [Time Series Contrastive Learning with Information-Aware Augmentations](https://mlsanity.com/paper/2303.11911/) (2023)
- [TimesURL: Self-supervised Contrastive Learning for Universal Time Series Representation Learning](https://mlsanity.com/paper/2312.15709/) (2023)
- [Deep Temporal Clustering : Fully Unsupervised Learning of Time-Domain Features](https://mlsanity.com/paper/1802.01059/) (2018)
- [MHCCL: Masked Hierarchical Cluster-Wise Contrastive Learning for Multivariate Time Series](https://mlsanity.com/paper/2212.01141/) (2022)
- [Strongly Augmented Contrastive Clustering](https://mlsanity.com/paper/2206.00380/) (2022)
- [Clustering Interval-Censored Time-Series for Disease Phenotyping](https://mlsanity.com/paper/2102.07005/) (2021)
- [TimeDRL: Disentangled Representation Learning for Multivariate Time-Series](https://mlsanity.com/paper/2312.04142/) (2023)
- [Time Series Clustering With Random Convolutional Kernels](https://mlsanity.com/paper/2305.10457/) (2023)
- [Time Series Clustering with an EM algorithm for Mixtures of Linear Gaussian State Space Models](https://mlsanity.com/paper/2208.11907/) (2022)
- [Deep Temporal Contrastive Clustering](https://mlsanity.com/paper/2212.14366/) (2022)
- [k-Graph: A Graph Embedding for Interpretable Time Series Clustering](https://mlsanity.com/paper/2502.13049/) (2025)

## Built on by (most cited first, 4 of 4)

- [Mask the Redundancy: Evolving Masking Representation Learning for Multivariate Time-Series Clustering](https://mlsanity.com/paper/2511.17008/) (2025)
- [TFEC: Multivariate Time-Series Clustering via Temporal-Frequency Enhanced Contrastive Learning](https://mlsanity.com/paper/2601.07550/) (2026)
- [FMMVCC: Fuzzy Mamba-based Multi-View Contrastive Clustering for Univariate Time Series](https://mlsanity.com/paper/2607.07258/) (2026)
- [Seeing Time: Visual-Temporal Representation Learning for Interpretable Time Series Clustering](https://mlsanity.com/paper/2609.36873/) (2026)

## Abstract (arXiv)

The rapid growth of unlabeled time series data, driven by the Internet of Things (IoT), poses significant challenges in uncovering underlying patterns. Traditional unsupervised clustering methods often fail to capture the complex nature of time series data. Recent deep learning-based clustering approaches, while effective, struggle with insufficient representation learning and the integration of clustering objectives. To address these issues, we propose a fuzzy cluster-aware contrastive clustering framework (FCACC) that jointly optimizes representation learning and clustering. Our approach introduces a novel three-view data augmentation strategy to enhance feature extraction by leveraging various characteristics of time series data. Additionally, we propose a cluster-aware hard negative sample generation mechanism that dynamically constructs high-quality negative samples using clustering structure information, thereby improving the model's discriminative ability. By leveraging fuzzy clustering, FCACC dynamically generates cluster structures to guide the contrastive learning process, resulting in more accurate clustering. Extensive experiments on 40 benchmark datasets show that FCACC outperforms the selected baseline methods (eight in total), providing an effective solution for unsupervised time series learning.