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How to test unsupervised learning methods for anomaly detection? I am looking for a test strategy to evaluate my result of my anomaly detection technique? what is your offer more than evaluate with different algorithms. My data is some time series very low frequency.

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    $\begingroup$ You might read this paper "How to Evaluate the Quality of Unsupervised Anomaly Detection Algorithms?" => arxiv.org/abs/1607.01152 $\endgroup$
    – user119783
    Commented Jun 25, 2021 at 18:32

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Each observation will have a "position" in your problem's feature-space.

  • If your data is already clustered, calculate the distance to the nearest cluster centers, and see how it compares to the average distance of transactions associated with those clusters.
  • If your data is not clustered, grab a random sample of data, calculate the distance to the N nearest neighbors; as new data comes in, look for cases where distance to nearest neighbors is significantly larger than average.

Ideally, the dimensions of feature-space should be normalized, so that each dimension of interest spans a similar length.

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  • $\begingroup$ Thanks but there are huge amount of data, and as a result, it does not make sense to use kmeans $\endgroup$ Commented Jul 14, 2021 at 15:59

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