방법 비교

	설명 가능한 K-평균	DBSCAN	K-평균 군집화
분야	머신러닝	머신러닝	머신러닝
계열	Machine learning	Machine learning	Machine learning
기원 연도	2020	1996	1967
창시자	Dasgupta, S.; Moshkovitz, M.; Frost, N.; Rashtchian, C.	Ester, M., Kriegel, H.-P., Sander, J. & Xu, X.	MacQueen, J.
유형	Explainable unsupervised clustering algorithm	Density-based clustering algorithm	Partitional clustering (centroid-based)
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.	Ester, M., Kriegel, H.-P., Sander, J. & Xu, X. (1996). A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise. Proceedings of the 2nd KDD, 226–231.	MacQueen, J. (1967). Some Methods for Classification and Analysis of Multivariate Observations. Proceedings of the 5th Berkeley Symposium on Mathematical Statistics and Probability, 1, 281–297.
별칭	ExKMC, interpretable k-means, decision-tree k-means, explainable clustering	DBSCAN Kümeleme, density-based clustering, density-based spatial clustering	K-Ortalamalar Kümeleme, k-ortalamalar kümeleme, k-means, centroid clustering
관련	5	3	3
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.	DBSCAN is a density-based clustering algorithm, introduced by Ester, Kriegel, Sander and Xu in 1996, that groups together points lying in dense regions and flags points in sparse regions as noise. It is effective on noisy data and on clusters of irregular, non-spherical shapes.	K-Means Clustering is a centroid-based partitional clustering algorithm, traced to J. MacQueen in 1967, that splits data into k clusters by assigning each observation to its nearest cluster centre. It is widely used for marketing segmentation, customer grouping, and exploratory analysis.