Deciding input values to DBSCAN algorithm

问题:

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?

回答1:

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).