DBSCAN (with metric only) in scikit-learn
I wrote my own distance code ref the top answer, just as it says, it was extremely slow, the built-in distance code was much better. I'm wondering how to speed up.
I have objects and a distance function, and want to cluster these using DBSCAN method in scikit-learn. My objects don't have a representation in Euclidean space. I know, that it is possible to useprecomputed metric, but in my case it's very impractical, due to large size of distance matrix. Is there any way to overcome this in scikit-learn?   Maybe, are there another python implementations of DBSCAN that can do so?