Deciding input values to DBSCAN algorithm
DBSCAN is pretty often hard to estimate its parameters.
Did you think about the OPTICS algorithm? You only need in this case Min_samples which would correspond to the minimal cluster size.
Otherwise for DBSCAN I've done it in the past by trial and error : try some values and see what happens. A general rule to follow is that if your dataset is noisy, you should have a larger value, and it is also correlated with the number of dimensions (10 in this case).
I have written code in python to implement DBSCAN clustering algorithm.
My dataset consists of 14k users with each user represented by 10 features.
I am unable to decide what exactly to keep as the value of Min_samples and epsilon as input
How should I decide that?
Similarity measure is euclidean distance.(Hence it becomes even more tough to decide.) Any pointers?