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.
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.