# A Rapid Review of Clustering Algorithms

arXiv: https://arxiv.org/abs/2401.07389 · HTML: https://mlsanity.com/paper/2401.07389/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 101 references that are in the map.
- First posted on arXiv 14 Jan 2024.

## Builds on (most cited first, 16 of 16)

- [Unsupervised Deep Embedding for Clustering Analysis](https://mlsanity.com/paper/1511.06335/) (2015)
- [Towards K-means-friendly Spaces: Simultaneous Deep Learning and Clustering](https://mlsanity.com/paper/1610.04794/) (2016)
- [Variational Deep Embedding: An Unsupervised and Generative Approach to Clustering](https://mlsanity.com/paper/1611.05148/) (2016)
- [Graph Embedded Pose Clustering for Anomaly Detection](https://mlsanity.com/paper/1912.11850/) (2019)
- [SpectralNet: Spectral Clustering using Deep Neural Networks](https://mlsanity.com/paper/1801.01587/) (2018)
- [A Comprehensive Survey on Deep Clustering: Taxonomy, Challenges, and Future Directions](https://mlsanity.com/paper/2206.07579/) (2022)
- [Hierarchical Clustering: Objective Functions and Algorithms](https://mlsanity.com/paper/1704.02147/) (2017)
- [Deep k-Means: Jointly clustering with k-Means and learning representations](https://mlsanity.com/paper/1806.10069/) (2018)
- [Clustering with Deep Learning: Taxonomy and New Methods](https://mlsanity.com/paper/1801.07648/) (2018)
- [Fast and Eager k-Medoids Clustering: O(k) Runtime Improvement of the PAM, CLARA, and CLARANS Algorithms](https://mlsanity.com/paper/2008.05171/) (2020)
- [Clustering Algorithms: A Comparative Approach](https://mlsanity.com/paper/1612.08388/) (2016)
- [Detecting Topic and Sentiment Dynamics Due to COVID-19 Pandemic Using Social Media](https://mlsanity.com/paper/2007.02304/) (2020)
- [Motif Clustering and Overlapping Clustering for Social Network Analysis](https://mlsanity.com/paper/1612.00895/) (2016)
- [Sentiment Analysis and Topic Modeling for COVID-19 Vaccine Discussions](https://mlsanity.com/paper/2111.04415/) (2021)
- [A Comparative study Between Fuzzy Clustering Algorithm and Hard Clustering Algorithm](https://mlsanity.com/paper/1404.6059/) (2014)
- [Representation Learning for Short Text Clustering](https://mlsanity.com/paper/2109.09894/) (2021)

## Built on by (most cited first, 9 of 9)

- [A note on the physical interpretation of neural PDE's](https://mlsanity.com/paper/2502.06739/) (2025)
- [Aircraft Trajectory Segmentation-based Contrastive Coding: A Framework for Self-supervised Trajectory Representation](https://mlsanity.com/paper/2407.20028/) (2024)
- [CLUBench: A Clustering Benchmark](https://mlsanity.com/paper/2605.29933/) (2026)
- [Enhancing Affinity Propagation for Improved Public Sentiment Insights](https://mlsanity.com/paper/2410.12862/) (2024)
- [Clustering with Uniformity- and Neighbor-Based Random Geometric Graphs](https://mlsanity.com/paper/2501.06268/) (2025)
- [Learning Augmented Graph k-Clustering](https://mlsanity.com/paper/2506.13533/) (2025)
- [Bilevel Models for Adversarial Learning and A Case Study](https://mlsanity.com/paper/2510.25121/) (2025)
- [Quasicrystal Architected Nanomechanical Resonators via Data-Driven Design](https://mlsanity.com/paper/2604.07379/) (2026)
- [Cohort Organized Learning: Clustering Through Agreement](https://mlsanity.com/paper/2606.21743/) (2026)

## Abstract (arXiv)

Clustering algorithms aim to organize data into groups or clusters based on the inherent patterns and similarities within the data. They play an important role in today's life, such as in marketing and e-commerce, healthcare, data organization and analysis, and social media. Numerous clustering algorithms exist, with ongoing developments introducing new ones. Each algorithm possesses its own set of strengths and weaknesses, and as of now, there is no universally applicable algorithm for all tasks. In this work, we analyzed existing clustering algorithms and classify mainstream algorithms across five different dimensions: underlying principles and characteristics, data point assignment to clusters, dataset capacity, predefined cluster numbers and application area. This classification facilitates researchers in understanding clustering algorithms from various perspectives and helps them identify algorithms suitable for solving specific tasks. Finally, we discussed the current trends and potential future directions in clustering algorithms. We also identified and discussed open challenges and unresolved issues in the field.