قارن الطرق

	DBSCAN	غابة العزل
المجال	تعلم الآلة	تعلم الآلة
العائلة	Machine learning	Machine learning
سنة النشأة	1996	2008
صاحب الطريقة	Ester, M., Kriegel, H.-P., Sander, J. & Xu, X.	Liu, F.T., Ting, K.M. & Zhou, Z.-H.
النوع	Density-based clustering algorithm	Unsupervised ensemble (random partitioning trees)
المصدر التأسيسي	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.	Liu, F.T., Ting, K.M. & Zhou, Z.-H. (2008). Isolation Forest. IEEE ICDM, 413–422.
الأسماء البديلة	DBSCAN Kümeleme, density-based clustering, density-based spatial clustering	Isolation Forest (Aykırı Değer Tespiti), iForest, isolation forest anomaly detection
ذات صلة	3	5
الملخص	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.	Isolation Forest is an unsupervised machine-learning method for anomaly and outlier detection, introduced by Liu, Ting and Zhou in 2008, that isolates anomalies through random partitioning of the data. It works without any labelled anomaly data and scales to high-dimensional datasets.