Záznam dôkazov metódy

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

Taxonomický záznam metódy · 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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Same method familyAutoencodermachine-suggested · Relational suggestion, not evidence.Same method familyDBSCANmachine-suggested · Relational suggestion, not evidence.Same method familyIsolation Forestmachine-suggested · Relational suggestion, not evidence.Same method familyOne-class SVMmachine-suggested · Relational suggestion, not evidence.