Machine learning
Local Outlier Factor (LOF)
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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Sources
- 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