Interpretable Clustering: A Survey

TL;DR

This survey reviews current explainable clustering algorithms, emphasizing the importance of interpretability in high-stakes domains and providing a structured taxonomy to guide research and application.

Contribution

It offers a comprehensive taxonomy and repository of interpretable clustering methods, aiding researchers in selecting suitable approaches for specific needs.

Findings

Identifies key criteria for explainable clustering methods

Provides a structured taxonomy of interpretability techniques

Offers an open repository of methods and tools

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. For convenient access and reference, an open repository organizes representative and emerging interpretable clustering methods under the taxonomy proposed in this survey, available at https://github.com/hulianyu/Awesome-Interpretable-Clustering