I have logs of user activity on my system. This is a CMS system.
The logs consist of:
- User ID
- Action Performed (one of 110 possible actions - things like page edited, page read, login, etc.)
- Site action was performed on (not relevant for all possible actions)
- Object action was performed on (document name or document id - not relevant for all possible actions)
- Date and time that the action was performed
I would like to use this data to cluster users into groups with similar behavior, so that I can then survey the groups to find out who they are and then target them with training interventions, newsletters etc.
I need help with converting the above list of things into clusters.
What techniques can I use to generate a matrix from the above data that I can feed into a K-Means cluster algorithm?
I tried counting action by user and using that to feed a cluster algorithm but the result was useless. I also tried treating the site and document ids as text, feature hashing that and feeding it into a K-Means cluster model, also got useless data out.