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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?