방법 비교

	설명 가능한 K-평균	결정 트리
분야	머신러닝	머신러닝
계열	Machine learning	Machine learning
기원 연도	2020	1984
창시자	Dasgupta, S.; Moshkovitz, M.; Frost, N.; Rashtchian, C.	Breiman, Friedman, Olshen & Stone
유형	Explainable unsupervised clustering algorithm	Recursive partitioning (if-then rules)
원전	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.	Breiman, L., Friedman, J.H., Olshen, R.A. & Stone, C.J. (1984). Classification and Regression Trees. Wadsworth.
별칭	ExKMC, interpretable k-means, decision-tree k-means, explainable clustering	Karar Ağacı (Decision Tree), karar ağacı, classification tree, regression tree
관련	5	5
요약	Explainable K-Means is a post-hoc and in-model interpretability approach to standard K-Means clustering that replaces or approximates cluster assignments with a small axis-aligned decision tree. Each leaf of the tree corresponds to one cluster, and every data point is assigned to a cluster by following a simple sequence of threshold rules on individual features — making cluster membership fully transparent and human-readable.	A Decision Tree is an interpretable classification and regression method, formalised by Breiman, Friedman, Olshen and Stone in their 1984 CART framework, that partitions the data with hierarchical if-then rules. Each split sends observations down one branch or another until a prediction is read off the leaf.