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
- 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
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
pyclustertend-1.3.3.tar.gz
(4.8 kB
view hashes)
Built Distribution
Close
Hashes for pyclustertend-1.3.3-py3-none-any.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | a88a401d361c81696b6e6df40a9d0eeb2dfacc3718165ab9db14355321968544 |
|
MD5 | dd0d61170cf271a5e73843b60fe3480b |
|
BLAKE2b-256 | 2d8799214bf8908c27ad46817cd65bcb0489db8573e6e9130c039479f7904669 |