梳理可解释聚类方法，帮研究者选对工具
Interpretable Clustering: A Survey
- 按可解释性标准分类现有聚类算法
- 提出评估方法适用性的关键指标
- 适合医疗金融等高风险场景研究者
近年来，聚类算法研究主要聚焦于提升准确率和效率，但常牺牲可解释性。随着这些方法在医疗、金融和自动驾驶等高风险领域应用日益广泛，透明且可解释的聚类结果已成为关键需求，不仅关乎用户信任，也满足伦理与监管要求。确保聚类决策可被清晰理解与合理辩护，现已成为基本前提。为此，本文系统综述了当前可解释聚类算法的研究现状，提出了区分不同方法的关键标准。这些洞察有助于研究人员根据具体应用场景，选择最合适的可解释聚类方法，同时推动高效且透明算法的发展与应用。为方便参考，一个开源仓库已按本文提出的分类体系整理代表性及新兴可解释聚类方法，网址为 https://hulianyu.xyz/Interpretable-Clustering-Repository。
原文摘要 · Abstract (English)
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 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://hulianyu.xyz/Interpretable-Clustering-Repository
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