Algorithms that find the few observations in a data set that do not fit the pattern of the rest, such as faulty readings or fraud. The local outlier factor compares how densely packed an item's neighbourhood is with the neighbourhoods of its neighbours, and the isolation forest isolates unusual items quickly by cutting the data at random using many trees, so that outliers need fewer cuts than ordinary points.
All Outlier Detection Algorithms
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Local outlier factor (Wikipedia)
Has Member: Local Outlier Factor Algorithm, LeadQuote, Has Member: Local Outlier Factor Algorithm, Lead
In anomaly detection, the local outlier factor (LOF) is an algorithm proposed by Markus M. Breunig, Hans-Peter Kriegel, Raymond T. Ng and Jörg Sander in 2000 for finding anomalous data points by measuring the local deviation of a given data point with respect to its neighbours.
scikit-learn: scikit-learn: Novelty and Outlier Detection
scikit-learnHas Member: Isolation Forest Algorithm, Section 2.7.3.2 Isolation ForestQuote, Has Member: Isolation Forest Algorithm, Section 2.7.3.2 Isolation Forest
One efficient way of performing outlier detection in high-dimensional datasets is to use random forests.
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