Linganisha mbinu

	DBSCAN	Explainable Isolation Forest
Nyanja	Ujifunzaji wa Mashine	Ujifunzaji wa Mashine
Familia	Machine learning	Machine learning
Mwaka wa asili	1996	2008 / 2017
Mwanzilishi	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)
Aina	Density-based clustering algorithm	Anomaly detection with post-hoc explainability
Chanzo asilia	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.
Majina mbadala	DBSCAN Kümeleme, density-based clustering, density-based spatial clustering	XIF, Isolation Forest with SHAP, interpretable anomaly detection, explainable anomaly isolation
Zinazohusiana	3	5
Muhtasari	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.