Methoden vergleichen

	Erklärbares K-Means	DBSCAN
Fachgebiet	Maschinelles Lernen	Maschinelles Lernen
Familie	Machine learning	Machine learning
Entstehungsjahr	2020	1996
Urheber	Dasgupta, S.; Moshkovitz, M.; Frost, N.; Rashtchian, C.	Ester, M., Kriegel, H.-P., Sander, J. & Xu, X.
Typ	Explainable unsupervised clustering algorithm	Density-based clustering algorithm
Wegweisende Quelle	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.
Aliasnamen	ExKMC, interpretable k-means, decision-tree k-means, explainable clustering	DBSCAN Kümeleme, density-based clustering, density-based spatial clustering
Verwandt	5	3
Zusammenfassung	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.