Compara mètodes

	[REQUIRES TRANSLATION]	DBSCAN	Arbre de decisió
Camp	Aprenentatge automàtic	Aprenentatge automàtic	Aprenentatge automàtic
Família	Machine learning	Machine learning	Machine learning
Any d'origen	2020	1996	1984
Autor original	Dasgupta, S.; Moshkovitz, M.; Frost, N.; Rashtchian, C.	Ester, M., Kriegel, H.-P., Sander, J. & Xu, X.	Breiman, Friedman, Olshen & Stone
Tipus	Explainable unsupervised clustering algorithm	Density-based clustering algorithm	Recursive partitioning (if-then rules)
Font seminal	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.	Breiman, L., Friedman, J.H., Olshen, R.A. & Stone, C.J. (1984). Classification and Regression Trees. Wadsworth.
Àlies	ExKMC, interpretable k-means, decision-tree k-means, explainable clustering	DBSCAN Kümeleme, density-based clustering, density-based spatial clustering	Karar Ağacı (Decision Tree), karar ağacı, classification tree, regression tree
Relacionats	5	3	5
Resum	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.	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.