I am using LOF ( local Outlier factor) to detect outliers in my data. I get LOF score as outlier distance. this unsupervised learning doesnt help to detect outliers at run time. So I want to use my data points and LOF score to have a supervised model [regression /classification].
My question is which should be approach out of
1) classification ( taking a cutoff of LOF score and after having binary variable , build classification model) and use this classification model to predict outliers at run time.
How does any clustering/anomaly detection( using clustering) is even used at run time ?? Any guidance will help.