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Interpretable Clustering: A Survey

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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

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Optimal classification trees

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Interpretable clustering: an optimization approach

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A survey on interpretable reinforcement learning

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Interpretable clustering using unsupervised binary trees

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Towards explainable community finding

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Explanation in artificial intelligence: Insights from the social sciences

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Explainable community detection

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Interpretable graph clustering on massive attribute networks via multi-agent dynamic game

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Global explainable clustering via equivalence relation

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Towards trustworthy artificial intelligence for decision-making: A lifecycle perspective on knowledge- and data-driven artificial intelligence systems

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On clustering and interpreting with rules by means of mathematical optimization

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TASC: Two-attribute-set clustering through decision tree construction

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Explainable Artificial Intelligence (XAI): What we know and what is left to attain Trustworthy Artificial Intelligence

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Explainable Artificial Intelligence (XAI) 2.0: A manifesto of open challenges and interdisciplinary research directions

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Interpretability research of deep learning: A literature survey

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Description-oriented community detection using exhaustive subgroup discovery

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Interpretable sequence clustering

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Significance-based decision tree for interpretable categorical data clustering

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Significance-based interpretable sequence clustering

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Mining patterns for clustering on numerical datasets using unsupervised decision trees

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A review of clustering techniques and developments

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Evaluating the necessity of the multiple metrics for assessing explainable AI: A critical examination

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inferredcites →https://doi.org/10.1016/j.patcog.2017.01.031openalex-snapshot

Clustering nominal data using unsupervised binary decision trees: Comparisons with the state of the art methods

inferredcites →https://doi.org/10.1016/j.patcog.2022.109239openalex-snapshot

Shallow decision trees for explainable k -means clustering

inferredcites →https://doi.org/10.1016/j.patcog.2024.110298openalex-snapshot

Fast and explainable clustering based on sorting

inferredcites →https://doi.org/10.1016/j.patcog.2025.111364openalex-snapshot

Interpretable categorical data clustering via hypothesis testing

inferredcites →https://doi.org/10.1016/j.patcog.2025.111418openalex-snapshot

Interpretable multi-view clustering

inferredcites →https://doi.org/10.1016/j.patrec.2009.09.011openalex-snapshot

Data clustering: 50 years beyond K-means

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Classification and regression trees

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Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead

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From local explanations to global understanding with explainable AI for trees

inferredcites →https://doi.org/10.1073/pnas.1900654116openalex-snapshot

Definitions, methods, and applications in interpretable machine learning

inferredcites →https://doi.org/10.1109/access.2018.2870052openalex-snapshot

Peeking Inside the Black-Box: A Survey on Explainable Artificial Intelligence (XAI)

inferredcites →https://doi.org/10.1109/tkde.2005.11openalex-snapshot

Interpretable hierarchical clustering by constructing an unsupervised decision tree

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A Diversified Attention Model for Interpretable Multiple Clusterings

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$k$-Graph: A Graph Embedding for Interpretable Time Series Clustering

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Minimal Transmit Power in Parallel Vector Broadcast Channels With Linear Precoding

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Description-Driven Community Detection

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An Improved Approximation for k -Median and Positive Correlation in Budgeted Optimization

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Explainable AI for Medical Data: Current Methods, Limitations, and Future Directions

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Benchmarking Instance-Centric Counterfactual Algorithms for XAI: From White Box to Black Box

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Toward Trustworthy Artificial Intelligence (TAI) in the Context of Explainability and Robustness

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Explanation of clustering result based on multi-objective optimization

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Explaining Black-Box Clustering Pipelines with Cluster-Explorer

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A Survey on the Explainability of Supervised Machine Learning

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The Disagreement Problem in Explainable Machine Learning: A Practitioner's Perspective

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Incoming claims: where this work appears in other papers’ reference lists and arguments.

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Interpretable clustering of PS-InSAR time series for ground deformation detection

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Interpretable graph clustering on massive attribute networks via multi-agent dynamic game

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Prototype-based interpretable community detection

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A unified approach to extract interpretable rules from tree ensembles via Integer Programming

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Biological sequence clustering: A survey

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Customer Segmentation Using Aggregated Retail Transaction Data: Examining the Role of Clustering Algorithm Choice

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Explainable Clustering of Skeleton Time-Series: AIME-Driven Feature Contribution Analysis of Gait Styles

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Hedge-Algebra Linguistic State Clustering for Interpretable Physical-State Monitoring in UAV-Enabled ISAC

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Cluster Analysis of Older Adults Based on Digital Competence Using Unsupervised Machine Learning

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Redundancy-Aware External Signal Learning for Image Clustering

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Examining Temporal Awareness Through Interpretable Clustering

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Supervised Clustered Interpretability: Explainable Subgroup Discovery via Cluster Separation and Partial Dependence Disparity

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Game Theory Meets Explainable AI: An Enhanced Approach to Understanding Black Box Models Through Shapley Values

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Modular Local Classification via Cluster-Guided Feature Selection in Tabular Data

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