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DBSCAN×Isolation Forest×One-Class SVM×
분야머신러닝머신러닝머신러닝
계열Machine learningMachine learningMachine learning
기원 연도199620081999–2001
창시자Ester, M., Kriegel, H.-P., Sander, J. & Xu, X.Liu, F.T., Ting, K.M. & Zhou, Z.-H.Scholkopf, B., Platt, J. C., Smola, A. J., Williamson, R. C.
유형Density-based clustering algorithmUnsupervised ensemble (random partitioning trees)Anomaly / novelty detection (unsupervised)
원전Ester, 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. link ↗Liu, F.T., Ting, K.M. & Zhou, Z.-H. (2008). Isolation Forest. IEEE ICDM, 413–422. DOI ↗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. DOI ↗
별칭DBSCAN Kümeleme, density-based clustering, density-based spatial clusteringIsolation Forest (Aykırı Değer Tespiti), iForest, isolation forest anomaly detectionOCSVM, one-class support vector machine, novelty SVM, unsupervised SVM
관련353
요약DBSCAN 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.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.
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ScholarGate방법 비교: DBSCAN · Isolation Forest · One-class SVM. 2026-06-18에 다음에서 검색함: https://scholargate.app/ko/compare