title: How to use sklearn's DBSCAN with a spherical metric?
body: <p>I have a set of data distributed on a sphere and I am trying to understand what metrics must be given to the function DBSCAN distributed by scikit-learn. It cannot be the Euclidean metrics, because the metric the points are distributed with is not Euclidean. Is there, in the sklearn packet, a metric implemented for such cases or is dividing the data in small subsets the easiest (if long and tedious) way to proceed?</p>
  
  <p>P.S. I am a noob at python</p>
  
  <p>P.P.S. In case I "precompute" the metric, in what form do I have to submit my precomputed data?
  Like this? </p>
  
  <p>0 - event1 - event2 - ...</p>
  
  <p>event1 - 0 - distance(event1,event2) - ...</p>
  
  <p>event2 - distance(event1,event2) - 0</p>
  
  <p>Please, help?</p>