# pyclustertend
[](https://travis-ci.com/lachhebo/pyclustertend) [](https://pypi.org/project/pyclustertend/) [](https://pyclustertend.readthedocs.io/en/master/) [](https://pepy.tech/project/pyclustertend) [](https://codecov.io/gh/lachhebo/pyclustertend)
[](https://zenodo.org/badge/latestdoi/187477036)
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 :
- [x] Hopkins Statistics
- [x] VAT
- [x] iVAT
- [x] Metric based method (silhouette, calinksi, davies bouldin)
## Installation
```shell
pip install pyclustertend
```
## Usage
### Example Hopkins
```python
>>>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
```python
>>>from sklearn import datasets
>>>from pyclustertend import vat
>>>from sklearn.preprocessing import scale
>>>X = scale(datasets.load_iris().data)
>>>vat(X)
```
<img height="350" src="https://raw.githubusercontent.com/lachhebo/pyclustertend/screenshots/vat.png" />
### Example iVat
```python
>>>from sklearn import datasets
>>>from pyclustertend import ivat
>>>from sklearn.preprocessing import scale
>>>X = scale(datasets.load_iris().data)
>>>ivat(X)
```
<img height="350" src="https://raw.githubusercontent.com/lachhebo/pyclustertend/screenshots/ivat.png" />
## Notes
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.
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"description": "# pyclustertend\n\n\n\n\n\n\n[](https://travis-ci.com/lachhebo/pyclustertend) [](https://pypi.org/project/pyclustertend/) [](https://pyclustertend.readthedocs.io/en/master/) [](https://pepy.tech/project/pyclustertend) [](https://codecov.io/gh/lachhebo/pyclustertend)\n[](https://zenodo.org/badge/latestdoi/187477036)\n\npyclustertend is a python package specialized in cluster tendency. Cluster tendency consist to assess if clustering algorithms are relevant for a dataset.\n\nThree methods for assessing cluster tendency are currently implemented and one additional method based on metrics obtained with a KMeans estimator :\n\n\n\n- [x] Hopkins Statistics\n- [x] VAT\n- [x] iVAT\n\n- [x] Metric based method (silhouette, calinksi, davies bouldin)\n\n## Installation\n\n```shell\n pip install pyclustertend\n```\n\n## Usage\n\n### Example Hopkins\n\n```python\n >>>from sklearn import datasets\n >>>from pyclustertend import hopkins\n >>>from sklearn.preprocessing import scale\n >>>X = scale(datasets.load_iris().data)\n >>>hopkins(X,150)\n 0.18950453452838564\n```\n\n### Example VAT\n\n```python\n >>>from sklearn import datasets\n >>>from pyclustertend import vat\n >>>from sklearn.preprocessing import scale\n >>>X = scale(datasets.load_iris().data)\n >>>vat(X)\n```\n\n<img height=\"350\" src=\"https://raw.githubusercontent.com/lachhebo/pyclustertend/screenshots/vat.png\" />\n\n### Example iVat\n\n```python\n >>>from sklearn import datasets\n >>>from pyclustertend import ivat\n >>>from sklearn.preprocessing import scale\n >>>X = scale(datasets.load_iris().data)\n >>>ivat(X)\n```\n\n<img height=\"350\" src=\"https://raw.githubusercontent.com/lachhebo/pyclustertend/screenshots/ivat.png\" />\n\n## Notes\n\nIt's preferable to scale the data before using hopkins or vat algorithm as they use distance between observations. Moreover, vat and ivat algorithms\ndo not really fit to massive databases. A first solution is to sample the data before using those algorithms. \n",
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