Machine learning
局部异常因子 (LOF)
局部异常因子 (LOF) 是Breunig、Kriegel、Ng和Sander于2000年推出的一种基于密度的无监督异常检测算法。它为每个数据点分配一个连续的异常分数，量化该点相对于其局部邻域的孤立程度，从而能够检测到全局方法因其在空间其他区域的密集簇中而可能遗漏的异常。
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来源
- 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
如何引用本页
ScholarGate. (2026, June 3). Local Outlier Factor (LOF): Density-Based Anomaly Detection. ScholarGate. https://scholargate.app/zh/machine-learning/local-outlier-factor
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