Σύγκριση μεθόδων

	DBSCAN	One-Class SVM
Πεδίο	Μηχανική Μάθηση	Μηχανική Μάθηση
Οικογένεια	Machine learning	Machine learning
Έτος προέλευσης	1996	1999–2001
Δημιουργός	Ester, M., Kriegel, H.-P., Sander, J. & Xu, X.	Scholkopf, B., Platt, J. C., Smola, A. J., Williamson, R. C.
Τύπος	Density-based clustering algorithm	Anomaly / novelty detection (unsupervised)
Θεμελιώδης πηγή	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.	Scholkopf, B., Platt, J. C., Shawe-Taylor, J., Smola, A. J., & Williamson, R. C. (2001). Estimating the support of a high-dimensional distribution. Neural Computation, 13(7), 1443–1471.
Εναλλακτικές ονομασίες	DBSCAN Kümeleme, density-based clustering, density-based spatial clustering	OCSVM, one-class support vector machine, novelty SVM, unsupervised SVM
Συναφείς	3	3
Σύνοψη	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.	One-class SVM is an unsupervised anomaly and novelty detection algorithm that learns a tight boundary around normal training data in a kernel-induced feature space, flagging new observations that fall outside that boundary as outliers. Introduced by Scholkopf et al. in 1999–2001, it extends the SVM framework to the single-class setting where no labelled anomalies are available.