#dbscan

Python implementation of 'Density Based Spatial Clustering of Applications with Noise'

Setup

python setup.py install

Usage

import dbscan
dbscan.dbscan(m, eps, min_points)

Documentation

┌───────────────────────────────────────────────────────────────────────────────────────────────┐
| dbscan.dbscan: (m, eps, min_points)
| Implementation of Density Based Spatial Clustering of Applications with Noise
|    See https://en.wikipedia.org/wiki/DBSCAN
| 
|    scikit-learn probably has a better implementation
|    Uses Euclidean Distance as the measure
| 
| Inputs:
| m - A matrix whose columns are feature vectors
| eps - Maximum distance two points can be to be regionally related
| min_points - The minimum number of points to make a cluster
| 
| Outputs:
| An array with either a cluster id number or dbscan.NOISE (None) for each 
| column vector in m.
└───────────────────────────────────────────────────────────────────────────────────────────────┘