Linganisha mbinu

	Explainable K-Means	DBSCAN	Ngeli ya Kiwango cha Juu (Hierarchical Clustering)
Nyanja	Ujifunzaji wa Mashine	Ujifunzaji wa Mashine	Ujifunzaji wa Mashine
Familia	Machine learning	Machine learning	Machine learning
Mwaka wa asili	2020	1996	1963
Mwanzilishi	Dasgupta, S.; Moshkovitz, M.; Frost, N.; Rashtchian, C.	Ester, M., Kriegel, H.-P., Sander, J. & Xu, X.	Ward, J. H.
Aina	Explainable unsupervised clustering algorithm	Density-based clustering algorithm	Unsupervised clustering (agglomerative)
Chanzo asilia	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.	Ward, J. H. (1963). Hierarchical Grouping to Optimize an Objective Function. Journal of the American Statistical Association, 58(301), 236–244.
Majina mbadala	ExKMC, interpretable k-means, decision-tree k-means, explainable clustering	DBSCAN Kümeleme, density-based clustering, density-based spatial clustering	Hiyerarşik Kümeleme, hiyerarşik kümeleme, agglomerative clustering, hierarchical agglomerative clustering
Zinazohusiana	5	3	4
Muhtasari	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.	Hierarchical clustering is an unsupervised method that groups observations into nested clusters and draws the result as a dendrogram, so the number of clusters need not be fixed in advance. Its agglomerative form rests on the objective-function grouping criterion introduced by Joe Ward in 1963.