I would like to know if there is any kind of research on the parameter selection (minpoints) for the HDBSCAN algorithm, in the context of having a training set with only normal samples and a test set that contains anomalies/outliers.
I want to apply the GLOSH algorithm to find the outliers but since i have a "clean" dataset available I suppose it is possible to exploit it.
I know that in the context of novelty detection exists lots of other algorithms (OC-SVM ecc...) but i would like to try solutions with HDBSCAN.
Hello Davide
Do you have interest on feture selection or hyper parameter optimization?
regards
hyper parameter optimization, in this specific case minpoints for HDBSCAN and the k params for the GLOSH algorithm
You may apply OPTIC for radius selection. Here there is no clear method for minpoint selection. We are also working on the topic.
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