방법 비교

	온라인 원클래스 SVM	지역 이상치 계수 (Local Outlier Factor, LOF)
분야	머신러닝	머신러닝
계열	Machine learning	Machine learning
기원 연도	2006 (incremental/online variant); 1999 (base method)	2000
창시자	Laskov, P. et al. (incremental extension); Scholkopf, B. et al. (original OC-SVM)	Breunig, M. M.; Kriegel, H.-P.; Ng, R. T.; Sander, J.
유형	Online anomaly detection / novelty detection	Density-based anomaly detection (unsupervised)
원전	Laskov, P., Gehl, C., Krueger, S., & Muller, K.-R. (2006). Incremental support vector learning: Analysis, implementation and applications. Journal of Machine Learning Research, 7, 1909–1936.	Breunig, M. M., Kriegel, H.-P., Ng, R. T., & Sander, J. (2000). LOF: Identifying density-based local outliers. Proceedings of the 2000 ACM SIGMOD International Conference on Management of Data, 93–104.
별칭	Online OC-SVM, Incremental One-Class SVM, Online SVDD, Sequential One-Class SVM	LOF, local outlier factor, density-based outlier detection, local density deviation
관련	4	4
요약	Online One-Class SVM is an incremental extension of the classical One-Class Support Vector Machine that updates its decision boundary as new data arrive one sample at a time, making it suitable for streaming environments and real-time anomaly or novelty detection without retraining from scratch.	Local Outlier Factor (LOF) is a density-based, unsupervised anomaly detection algorithm introduced by Breunig, Kriegel, Ng, and Sander in 2000. It assigns each data point a continuous outlier score that quantifies how isolated that point is relative to its local neighborhood, enabling detection of anomalies that global methods miss because they blend into dense clusters elsewhere in the space.