Comparar métodos

	DBSCAN	Isolation Forest Explicável	K-Vizinhos Mais Próximos Explicável
Área	Aprendizado de máquina	Aprendizado de máquina	Aprendizado de máquina
Família	Machine learning	Machine learning	Machine learning
Ano de origem	1996	2008 / 2017	1967 (KNN); 2010s (explainability extensions)
Autor original	Ester, M., Kriegel, H.-P., Sander, J. & Xu, X.	Liu, F. T., Ting, K. M., & Zhou, Z.-H. (Isolation Forest); Lundberg, S. M. & Lee, S.-I. (SHAP explainability layer)	Cover, T. & Hart, P. (KNN); XAI extensions by various authors
Tipo	Density-based clustering algorithm	Anomaly detection with post-hoc explainability	Instance-based learning with explainability layer
Fonte seminal	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.	Lundberg, S. M., & Lee, S.-I. (2017). A unified approach to interpreting model predictions. Advances in Neural Information Processing Systems, 30, 4765–4774.	Cover, T. & Hart, P. (1967). Nearest neighbor pattern classification. IEEE Transactions on Information Theory, 13(1), 21–27.
Outros nomes	DBSCAN Kümeleme, density-based clustering, density-based spatial clustering	XIF, Isolation Forest with SHAP, interpretable anomaly detection, explainable anomaly isolation	XKNN, Interpretable KNN, Explainable KNN, Transparent K-Nearest Neighbors
Relacionados	3	5	4
Resumo	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.	Explainable Isolation Forest combines the Isolation Forest anomaly detection algorithm with post-hoc explainability tools — most commonly SHAP (SHapley Additive exPlanations) — to not only flag anomalous observations but also reveal which features drove each anomaly score. It bridges unsupervised anomaly detection with the interpretability demands of regulated and high-stakes domains.	Explainable K-Nearest Neighbors (XKNN) augments the classic KNN classifier or regressor with structured post-hoc or built-in explanation mechanisms, exposing which retrieved neighbors, which features, and which distance contributions drive each individual prediction — making the model's reasoning transparent and auditable for human decision-makers.