I was thinking DBSCAN or OPTICS could also be used when the number of categories is unknown, although I am no expert when it comes to understanding what sample size is necessary.
Scikit-learn already has a number of techniques implemented to do this, though they don't scale to large numbers of data points: Affinity Propogation and Mean Shift (mentioned in the cheat sheet). As far as I remember, DBSCAN also does this, but I'm not completely sure.
Yes but the current implementation of DBSCAN in sklearn is not very scalable. I think some contributors want to fix this but I am not sure what the current status is.
Comments
I was thinking DBSCAN or OPTICS could also be used when the number of categories is unknown, although I am no expert when it comes to understanding what sample size is necessary.
Scikit-learn already has a number of techniques implemented to do this, though they don't scale to large numbers of data points: Affinity Propogation and Mean Shift (mentioned in the cheat sheet). As far as I remember, DBSCAN also does this, but I'm not completely sure.
Yes but the current implementation of DBSCAN in sklearn is not very scalable. I think some contributors want to fix this but I am not sure what the current status is.