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