mlsanityData to 7 Oct 2026

A Rapid Review of Clustering Algorithms

arXiv 2401.07389 ↗First posted 14 Jan 2024cs.LG cs.AIcited by 9

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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.

Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). Builds on 16 of its 101 references that are in the map. First posted on arXiv 14 Jan 2024.

Builds on

16 of 101 references.

References · 16

  1. 2015Unsupervised Deep Embedding for Clustering Analysiscited by 763
  2. 2016Towards K-means-friendly Spaces: Simultaneous Deep Learning and Clusteringcited by 237
  3. 2016Variational Deep Embedding: An Unsupervised and Generative Approach to Clusteringcited by 219
  4. 2019Graph Embedded Pose Clustering for Anomaly Detectioncited by 95
  5. 2018SpectralNet: Spectral Clustering using Deep Neural Networkscited by 94
  6. 2022A Comprehensive Survey on Deep Clustering: Taxonomy, Challenges, and Future Directionscited by 73
  7. 2017Hierarchical Clustering: Objective Functions and Algorithmscited by 60
  8. 2018Deep k-Means: Jointly clustering with k-Means and learning representationscited by 50
  9. 2018Clustering with Deep Learning: Taxonomy and New Methodscited by 45
  10. 2020Fast and Eager k-Medoids Clustering: O(k) Runtime Improvement of the PAM, CLARA, and CLARANS Algorithmscited by 35
  11. 2016Clustering Algorithms: A Comparative Approachcited by 28
  12. 2020Detecting Topic and Sentiment Dynamics Due to COVID-19 Pandemic Using Social Mediacited by 12
4 more
  1. 2016Motif Clustering and Overlapping Clustering for Social Network Analysiscited by 8
  2. 2021Sentiment Analysis and Topic Modeling for COVID-19 Vaccine Discussionscited by 7
  3. 2014A Comparative study Between Fuzzy Clustering Algorithm and Hard Clustering Algorithmcited by 5
  4. 2021Representation Learning for Short Text Clusteringcited by 3

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 2; 2025: 4; 2026: 3.

9 papers.

  1. 2025A note on the physical interpretation of neural PDE'scited by 7
  2. 2024Aircraft Trajectory Segmentation-based Contrastive Coding: A Framework for Self-supervised Trajectory Representationcited by 2
  3. 2026CLUBench: A Clustering Benchmarkcited by 1
  4. 2024Enhancing Affinity Propagation for Improved Public Sentiment Insightscited by 0
  5. 2025Clustering with Uniformity- and Neighbor-Based Random Geometric Graphscited by 0
  6. 2025Learning Augmented Graph k-Clusteringcited by 0
  7. 2025Bilevel Models for Adversarial Learning and A Case Studycited by 0
  8. 2026Quasicrystal Architected Nanomechanical Resonators via Data-Driven Designcited by 0
  9. 2026Cohort Organized Learning: Clustering Through Agreementcited by 0