A package to assess cluster tendency
Project description
pyclustertend
Presentation :
pyclustertend is a python package to do cluster tendency. Cluster tendency consist to assess if clustering algorithms are relevant for a dataset.
Three methods for assessing cluster tendency are currently implemented :
- Hopkins Statistics
- VAT
- iVAT
- Metric based method (silhouette, calinksi, davies bouldin)
Installation :
pip install pyclustertend
Usage :
Example Hopkins :
>>>from sklearn import datasets
>>>from pyclustertend import hopkins
>>>from sklearn.preprocessing import scale
>>>X = scale(datasets.load_iris().data)
>>>hopkins(X,150)
0.18950453452838564
Example VAT :
>>>from sklearn import datasets
>>>from pyclustertend import vat
>>>from sklearn.preprocessing import scale
>>>X = scale(datasets.load_iris().data)
>>>vat(X)
Example Metric :
>>>from sklearn import datasets
>>>from pyclustertend import assess_tendency_by_metrics
>>>from sklearn.preprocessing import scale
>>>X = scale(datasets.load_iris().data)
>>>assess_tendency_by_metrics(X)
2.0
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