Method evidence record
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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Source record
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Local Outlier Factor (LOF): Density-Based Anomaly Detection
Taxonomic method recordml-modelmachine-learning
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
Open full method3 recorded citations, copied from the method source record.
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Related methods
Suggested4
Generated from the method graph and shown as machine-suggested relations. No evidence claim is inferred.
- Same method familyAutoencoder
machine-suggestedRelational suggestion, not evidence.
- Same method familyDBSCAN
machine-suggestedRelational suggestion, not evidence.
- Same method familyIsolation Forest
machine-suggestedRelational suggestion, not evidence.
- Same method familyOne-class SVM
machine-suggestedRelational suggestion, not evidence.