DBSCAN in Python

October 2017

Overview

In this README, we'll walk through the dbscan.py example code to show how the algorithm works.

Original data on the left and clusters identified by the DBSCAN algorithm on the right. For DBSCAN clusters, large colored points represent core cluster members, small colored points represent cluster edge members, and small black points represent outliers.

Here is a very cool tool, built by Naftali Harris, for helping to visualize DBSCAN clustering!

The Wiki Page for DBSCAN may also be useful to provide some background

DBSCAN Algorithm (Density-Based Spatial Clustering of Applications with Noise)

Sometimes called Euclidean Clustering

DBSCAN is a nice alternative to k-means when you don't know how many clusters to expect in your data, but you do know something about how the points should be clustered in terms of density (distance between points in a cluster).

DBSCAN datapoints do not have to be spatial data; they can be color data, intensity values, or other numerical features! This means we can cluster not only based upon proximity, but we can cluster similarly colored objects!