Machine learningMachine learning

可解释K-Means

可解释K-Means是一种针对标准K-Means聚类的后验（post-hoc）和模型内（in-model）可解释性方法，它用一个小的轴对齐决策树来替代或近似聚类分配。树的每个叶子对应一个聚类，每个数据点通过遵循一系列简单的个体特征阈值规则被分配到某个聚类中——这使得聚类成员身份完全透明且人类可读。

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The neighbourhood of related methods — select a node to explore.

可解释K-Means

Dasgupta, S., Frost, N., Moshkovitz, M., & Rashtchian, C. (2020). Explainability of k-Means Clustering. Proceedings of the 37th International Conference on Machine Learning (ICML), PMLR 119. link ↗
Moshkovitz, M., Dasgupta, S., Rashtchian, C., & Frost, N. (2020). Explainable k-Means and k-Medians Clustering. Proceedings of the 37th International Conference on Machine Learning (ICML), PMLR 119. link ↗

ScholarGate. (2026, June 3). Explainable K-Means Clustering. ScholarGate. https://scholargate.app/zh/machine-learning/explainable-k-means

Set this method beside its closest kin and read them side by side — the library lays the books on the table; the choice is yours.