The technique is essentially a density based outlier detection algorithm that, instead of calculating local densities, constructs a graph of the data using nearest-neighbors. The algorithm is different from other kNN outlier detection algorithms in that instead of setting ‘k’ as a parameter, you instead set a maximal inter-observation distance (called the graph “resolution” by Gartley and Basener). If the distance between two points is less than the graph resolution, add an edge between those two observations to the graph. Once the full graph is constructed, determine which connected components comprise the “background” of the data by setting some threshold percentage of observations ‘p’: any components with fewer than ‘p’ observations is considered an anomalous component, and all the observations (nodes) in this component are outliers. … Topological Anomaly Detection (TAD)

# If you did not already know: “Topological Anomaly Detection (TAD)”

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Oct 2015

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