To prevent spam users, you can only post on this forum after registration, which is by invitation. If you want to post on the forum, please send me a mail (h DOT m DOT w DOT verbeek AT tue DOT nl) and I'll send you an invitation in return for an account.

Distinction between mechine learning models and process models

I am having a confusion about machine learning models and process models. What I know is machine learning models are for prediction but on contrary process models are the ones that are the actual models based upon the actual data.

I need further clear cut distinction here. We perform feature selection in machine learning to predict variables that result in best model accuracy. What equal analogy exist in process mining. I mean let say I want to divide the event log data for clustering the traces of an event log. Can I use machine learning features extraction and feature selection techniques or I have to develop custom feature extraction and selection techniques that leads to selection of best features. What should be the criteria to evaluate features selection for best clustering?


Sign In or Register to comment.