pyclustertend is a python package specialized in cluster tendency. Cluster tendency consist to assess if clustering algorithms are relevant for a dataset.
Three methods for assessing cluster tendency are currently implemented and one additional method based on metrics obtained with a KMeans estimator :
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Hopkins Statistics
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VAT
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iVAT
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Metric based method (silhouette, calinksi, davies bouldin)
pip install pyclustertend >>>from sklearn import datasets
>>>from pyclustertend import hopkins
>>>from sklearn.preprocessing import scale
>>>X = scale(datasets.load_iris().data)
>>>hopkins(X,150)
0.18950453452838564 >>>from sklearn import datasets
>>>from pyclustertend import vat
>>>from sklearn.preprocessing import scale
>>>X = scale(datasets.load_iris().data)
>>>vat(X)
>>>from sklearn import datasets
>>>from pyclustertend import ivat
>>>from sklearn.preprocessing import scale
>>>X = scale(datasets.load_iris().data)
>>>ivat(X)
It's preferable to scale the data before using hopkins or vat algorithm as they use distance between observations. Moreover, vat and ivat algorithms do not really fit to massive databases. A first solution is to sample the data before using those algorithms.