I have been trying the find the best support and confidence values for associative rules mining. I came across the following approach from an answer on Quora - Picking the appropriate values for support and confidence can be  difficult, as it is very much an unsupervised process.However, if you transform the output of Apriori algorithm (association  rules) into features for a supervised machine learning algorithm, you  can examine the effect of having different support and confidences  values (while having other features fixed) on the performance of that  supervised model (ROC, RMSE, and etc.). Then, the best values for  support and confidence are the values that maximize, or minimize, the  performance metric.Being a rookie in this field, I am not sure on how to do this. Could you please help me on this?
Using classification to find the best support and confidence measure in associative rule mining
data mining;classification;unsupervised learning