# Data Stream Clustering: A Review

arXiv: https://arxiv.org/abs/2007.10781 · HTML: https://mlsanity.com/paper/2007.10781/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Jul 2020.

## Built on by (most cited first, 14 of 14)

- [Learning Self-Growth Maps for Fast and Accurate Imbalanced Streaming Data Clustering](https://mlsanity.com/paper/2404.09243/) (2024)
- [Sampling in Dirichlet Process Mixture Models for Clustering Streaming Data](https://mlsanity.com/paper/2202.13312/) (2022)
- [A Parameter-free Adaptive Resonance Theory-based Topological Clustering Algorithm Capable of Continual Learning](https://mlsanity.com/paper/2305.01507/) (2023)
- [Incremental cluster validity index-guided online learning for performance and robustness to presentation order](https://mlsanity.com/paper/2108.07743/) (2021)
- [Exponentially Consistent Nonparametric Linkage-Based Clustering of Data Sequences](https://mlsanity.com/paper/2411.13922/) (2024)
- [AutoStreamPipe: LLM Assisted Automatic Generation of Data Stream Processing Pipelines](https://mlsanity.com/paper/2510.23408/) (2025)
- [A Comprehensive Review of Machine Learning Advances on Data Change: A Cross-Field Perspective](https://mlsanity.com/paper/2402.12627/) (2024)
- [Redefining Event Types and Group Evolution in Temporal Data](https://mlsanity.com/paper/2403.06771/) (2024)
- [When Rules Fall Short: Agent-Driven Discovery of Emerging Content Issues in Short Video Platforms](https://mlsanity.com/paper/2601.11634/) (2026)
- [SECLEDS: Sequence Clustering in Evolving Data Streams via Multiple Medoids and Medoid Voting](https://mlsanity.com/paper/2206.12190/) (2022)
- [An efficient and straightforward online quantization method for a data stream through remove-birth updating](https://mlsanity.com/paper/2306.12574/) (2023)
- [Incremental Gaussian Mixture Clustering for Data Streams](https://mlsanity.com/paper/2412.07217/) (2024)
- [TNStream: Applying Tightest Neighbors to Micro-Clusters to Define Multi-Density Clusters in Streaming Data](https://mlsanity.com/paper/2505.00359/) (2025)
- [Single-pass Possibilistic Clustering with Damped Window Footprints](https://mlsanity.com/paper/2603.06889/) (2026)

## Abstract (arXiv)

Number of connected devices is steadily increasing and these devices continuously generate data streams. Real-time processing of data streams is arousing interest despite many challenges. Clustering is one of the most suitable methods for real-time data stream processing, because it can be applied with less prior information about the data and it does not need labeled instances. However, data stream clustering differs from traditional clustering in many aspects and it has several challenging issues. Here, we provide information regarding the concepts and common characteristics of data streams, such as concept drift, data structures for data streams, time window models and outlier detection. We comprehensively review recent data stream clustering algorithms and analyze them in terms of the base clustering technique, computational complexity and clustering accuracy. A comparison of these algorithms is given along with still open problems. We indicate popular data stream repositories and datasets, stream processing tools and platforms. Open problems about data stream clustering are also discussed.