Interpretable Clustering: A Survey

In recent years, much of the research on clustering algorithms has primarily focused on enhancing their accuracy and efficiency, frequently at the expense of interpretability. However, as these methods are increasingly being applied in high-stakes domains such as healthcare, finance, and autonomous systems, the need of transparent and interpretable clustering outcomes has become a critical concern. This is not only necessary for gaining user trust but also for satisfying the growing ethical and

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ethics.ai (16 January 2026), “Interpretable Clustering: A Survey,” evidence record 9889, https://ethics.ai/record/9889 (originally published by OpenAlex).

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