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Local Outlier Factor#

Local Outlier Factor (LOF) measures the local deviation of density of an unknown sample with respect to its k nearest neighbors from the training set. As such, LOF only considers the neighborhood of an unknown sample which enables it to detect anomalies within individual clusters of data.

Interfaces: Estimator, Learner, Scoring, Persistable

Data Type Compatibility: Depends on distance kernel


# Name Default Type Description
1 k 20 int The k nearest neighbors that form a local region.
2 contamination null float The proportion of outliers that are assumed to be present in the training set.
3 tree KDTree Spatial The spatial tree used to run nearest neighbor searches.


use Rubix\ML\AnomalyDetectors\LocalOutlierFactor;
use Rubix\ML\Graph\Trees\BallTree;
use Rubix\ML\Kernels\Distance\Euclidean;

$estimator = new LocalOutlierFactor(20, 0.1, new BallTree(30, new Euclidean));

Additional Methods#

Return the base spatial tree instance:

public tree() : Spatial


  1. M. M. Breunig et al. (2000). LOF: Identifying Density-Based Local Outliers.