방법 비교
선택한 방법을 나란히 검토하세요. 서로 다른 행은 강조 표시됩니다.
| 지역 이상치 계수 (Local Outlier Factor, LOF)× | Isolation Forest× | |
|---|---|---|
| 분야 | 머신러닝 | 머신러닝 |
| 계열 | Machine learning | Machine learning |
| 기원 연도≠ | 2000 | 2008 |
| 창시자≠ | Breunig, M. M.; Kriegel, H.-P.; Ng, R. T.; Sander, J. | Liu, F.T., Ting, K.M. & Zhou, Z.-H. |
| 유형≠ | Density-based anomaly detection (unsupervised) | Unsupervised ensemble (random partitioning trees) |
| 원전≠ | 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. DOI ↗ | Liu, F.T., Ting, K.M. & Zhou, Z.-H. (2008). Isolation Forest. IEEE ICDM, 413–422. DOI ↗ |
| 별칭≠ | LOF, local outlier factor, density-based outlier detection, local density deviation | Isolation Forest (Aykırı Değer Tespiti), iForest, isolation forest anomaly detection |
| 관련≠ | 4 | 5 |
| 요약≠ | 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. | 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. |
| ScholarGate데이터셋 ↗ |
|
|