Comparar métodos

	K-Means Explicável	Agrupamento Hierárquico
Área	Aprendizado de máquina	Aprendizado de máquina
Família	Machine learning	Machine learning
Ano de origem	2020	1963
Autor original	Dasgupta, S.; Moshkovitz, M.; Frost, N.; Rashtchian, C.	Ward, J. H.
Tipo	Explainable unsupervised clustering algorithm	Unsupervised clustering (agglomerative)
Fonte 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.	Ward, J. H. (1963). Hierarchical Grouping to Optimize an Objective Function. Journal of the American Statistical Association, 58(301), 236–244.
Outros nomes	ExKMC, interpretable k-means, decision-tree k-means, explainable clustering	Hiyerarşik Kümeleme, hiyerarşik kümeleme, agglomerative clustering, hierarchical agglomerative clustering
Relacionados	5	4
Resumo	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.	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.