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Local Outlier Factor
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.
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Local Outlier Factor (LOF): Density-Based Anomaly Detection
Taksonomski zapis metode · ml-model / machine-learning
- 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 10.1145/335191.335388
- Aggarwal, C. C. (2017). Outlier Analysis (2nd ed., Ch. 4). Springer. · ISBN 978-3-319-47577-6
- Hastie, T., Tibshirani, R., & Friedman, J. (2009). The Elements of Statistical Learning (2nd ed., Ch. 14). Springer. · ISBN 978-0-387-84857-0
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