Uporedite metode

	DBSCAN	Objašnjivi Izolacioni Šum	Objašnjivi K-najbližih suseda	HDBSCAN
Oblast	Mašinsko učenje	Mašinsko učenje	Mašinsko učenje	Mašinsko učenje
Porodica	Machine learning	Machine learning	Machine learning	Machine learning
Godina nastanka	1996	2008 / 2017	1967 (KNN); 2010s (explainability extensions)	2013
Tvorac	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	Campello, R. J. G. B.; Moulavi, D.; Sander, J.
Tip	Density-based clustering algorithm	Anomaly detection with post-hoc explainability	Instance-based learning with explainability layer	Hierarchical density-based clustering
Temeljni izvor	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.	Campello, R. J. G. B., Moulavi, D., & Sander, J. (2013). Density-Based Clustering Based on Hierarchical Density Estimates. In J. Pei et al. (Eds.), Advances in Knowledge Discovery and Data Mining. PAKDD 2013. Lecture Notes in Computer Science, vol. 7819 (pp. 160–172). Springer, Berlin, Heidelberg.
Drugi nazivi	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	HDBSCAN, Hierarchical DBSCAN, hierarchical density-based clustering, HDBSCAN*
Srodne	3	5	4	3
Sažetak	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.	HDBSCAN (Hierarchical Density-Based Spatial Clustering of Applications with Noise) is a density-based clustering algorithm introduced by Campello, Moulavi, and Sander in 2013. It extends DBSCAN by building a full hierarchy of density-based clusters across all density scales and then extracting a stable flat partition, making it robust to datasets where cluster densities vary substantially across regions.