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Local Outlier Factor

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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.

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How to read this: the white-ringed node is Local Outlier Factor. Surrounding nodes are direct relationships; the second ring is what those neighbors connect to. Edge thickness scales with source-article evidence. Click any node and choose Center graph here to walk the graph.