CRAD-Clustering

Python implementation of CRAD clustering algorithm (CRAD.py) and extended DBSCAN algorithm using CRAD framework (ExtensionToDBSCAN.py).

Setup

python setup.py install

Documentation

For CRAD-Clustering:

Call the function cal_adjM_cutOff(xxDist, StepSize, Nbin) to calculate adjancey matrix where

Inputs:

xxDist - A distance matrix using robust mahalanobis distance.

StepSize - Maximum steps of neighborhood you check in histogram to find optimal cut-off parameter.

Nbin - Number of bin in histogram to find optimal cut-off parameter.

Outputs:

An adjancey matrix

Then you call function clustering_(data, adj) to get the final clustering result, where

Inputs:

data - A m by n matrix where m corresponds to number of observations, and n corresponds to number of features.

adj - An adjancey matrix which is calculated in above step.

Output:

An array with either a cluster id number.

For Extended DBSCAN:

Call the function dbscan_newM(xxDist, StepSize, Nbin, min_points) where

Inputs:

xxDist - A distance matrix using robust mahalanobis distance

StepSize - Maximum steps of neighborhood you check in histogram to find optimal cut-off parameter

Nbin - Number of bin in histogram to find optimal cut-off parameter

min_points - The minimum number of points to make a cluster

Outputs:

An array with either a cluster id number.