השוואת שיטות

	DBSCAN מוסבר	DBSCAN	Explainable Isolation Forest	K-Nearest Neighbors מוסבר (Explainable K-Nearest Neighbors)
תחום	למידת מכונה	למידת מכונה	למידת מכונה	למידת מכונה
משפחה	Machine learning	Machine learning	Machine learning	Machine learning
שנת המקור	1996 (DBSCAN); 2010s (XAI integration)	1996	2008 / 2017	1967 (KNN); 2010s (explainability extensions)
הוגה השיטה	Ester, M. et al. (DBSCAN); XAI layer via Lundberg & Lee (SHAP)	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
סוג	Unsupervised clustering with post-hoc interpretability	Density-based clustering algorithm	Anomaly detection with post-hoc explainability	Instance-based learning with explainability layer
מקור מכונן	Ester, M., Kriegel, H.-P., Sander, J., & Xu, X. (1996). A density-based algorithm for discovering clusters in large spatial databases with noise. In Proceedings of the Second International Conference on Knowledge Discovery and Data Mining (KDD-96), 226–231. AAAI Press.	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.
כינויים	XAI-DBSCAN, interpretable DBSCAN, transparent density clustering, DBSCAN with post-hoc explanation	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
קשורות	5	3	5	4
תקציר	Explainable DBSCAN pairs the DBSCAN density-based clustering algorithm with post-hoc interpretability methods — most commonly SHAP values or local surrogate models — to reveal which input features drive the algorithm's cluster and noise assignments. It enables analysts to understand why specific points were grouped together or flagged as outliers, bridging the gap between powerful density-based partitioning and human-readable explanation.	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.