https://github.com/lachhebo/pyclustertend

A python package to assess cluster tendency
https://github.com/lachhebo/pyclustertend

cluster-analysis cluster-tendency clustering clustertendency data-science hopkins ivat machine-learning python scikit-learn statistics vat visual-assessment-cluster-tendency

Last synced: 2 months ago
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A python package to assess cluster tendency

• Host: GitHub
• URL: https://github.com/lachhebo/pyclustertend
• Owner: lachhebo
• License: bsd-3-clause
• Created: 2019-05-19T12:58:23.000Z (over 5 years ago)
• Default Branch: master
• Last Pushed: 2022-12-27T17:39:21.000Z (almost 2 years ago)
• Last Synced: 2024-04-25T10:44:14.638Z (8 months ago)
• Topics: cluster-analysis, cluster-tendency, clustering, clustertendency, data-science, hopkins, ivat, machine-learning, python, scikit-learn, statistics, vat, visual-assessment-cluster-tendency
• Language: Python
• Homepage: https://pyclustertend.readthedocs.io/en/master/index.html
• Size: 6.47 MB
• Stars: 43
• Watchers: 3
• Forks: 8
• Open Issues: 6
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# pyclustertend

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[![DOI](https://zenodo.org/badge/187477036.svg)](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)

```



### Example iVat

```python

    >>>from sklearn import datasets

    >>>from pyclustertend import ivat

    >>>from sklearn.preprocessing import scale

    >>>X = scale(datasets.load_iris().data)

    >>>ivat(X)

```



## 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.