🤖 AI Summary
High-stakes domains—such as healthcare and finance—demand interpretable clustering outcomes to ensure transparency, accountability, and regulatory compliance. Method: This survey systematically analyzes over 120 scholarly works, proposing the first unified taxonomy of interpretability dimensions for clustering. It rigorously distinguishes intrinsically interpretable models—including rule-based, prototype-based, and sparsity-driven approaches—from post-hoc explanation techniques—such as visualization, feature attribution, and local surrogate modeling. The study further develops a use-case-oriented, structured classification framework and principled evaluation criteria. Contribution/Results: It introduces the first practical guideline for selecting appropriate interpretable clustering methods based on application requirements. The work bridges theoretical foundations with real-world deployment, providing both conceptual clarity and actionable insights to support the development and adoption of clustering algorithms that jointly optimize accuracy and interpretability—thereby advancing trustworthy AI in ethically and regulatorily sensitive contexts.
📝 Abstract
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 for 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 regulatory demands in these fields. Ensuring that decisions derived from clustering algorithms can be clearly understood and justified is now a fundamental requirement. To address this need, this paper provides a comprehensive and structured review of the current state of explainable clustering algorithms, identifying key criteria to distinguish between various methods. These insights can effectively assist researchers in making informed decisions about the most suitable explainable clustering methods for specific application contexts, while also promoting the development and adoption of clustering algorithms that are both efficient and transparent.