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Interpretable Clustering Repository

A curated repository of literature on interpretable and explainable clustering, structured around our comprehensive survey published in ACM Computing Surveys (2026). Covers in-clustering and post-clustering interpretability, from decision-tree methods to rule-based, prototype, and many other interpretable clustering models worth exploring and applying.

πŸ—‚οΈ Taxonomy of Interpretable Clustering
Taxonomy diagram
🧩 Interpretable Clustering Models Overview
Models overview
Paper Repository
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Cite This Work

If this repository or our survey helped your research, please consider citing the paper: Interpretable Clustering: A Survey

@article{Hu2026xclu,
  title     = {Interpretable Clustering: A Survey},
  author    = {Hu, Lianyu and Jiang, Mudi and Dong, Junjie and Liu, Xinying and He, Zengyou},
  journal   = {ACM Computing Surveys},
  year      = {2026},
  volume    = {58},
  number    = {8},
  articleno = {215},
  publisher = {Association for Computing Machinery},
  doi       = {10.1145/3789495},
  keywords  = {interpretable clustering, algorithmic interpretability, interpretable machine learning, Explainable AI},
}