sklearn: Get Distance from Point to Nearest Cluster
To be closer to the intuition of DBSCAN you probably should only consider core points.
Put the core points into a nearest neighbor searcher. Then search for all noise points, use the cluster label of the nearest point.
I'm using clustering algorithms like DBSCAN.
It returns a 'cluster' called -1 which are points that are not part of any cluster. For these points I want to determine the distance from it to the nearest cluster to get something like a metric for how abnormal this point is. Is this possible? Or are there any alternatives for this kind of metric?