Metadata-Version: 2.1
Name: pyclustertend
Version: 1.3.3
Summary: A package to assess cluster tendency
Home-page: https://github.com/lachhebo/pyclustertend
Author: Ismaël Lachheb
Author-email: ismael.lachheb@protonmail.com
License: UNKNOWN
Description: # pyclustertend [![Build Status](https://travis-ci.com/lachhebo/pyclustertend.svg?branch=master)](https://travis-ci.com/lachhebo/pyclustertend)  [![PyPi Status](https://img.shields.io/pypi/v/pyclustertend.svg?color=brightgreen)](https://pypi.org/project/pyclustertend/) [![Documentation Status](https://readthedocs.org/projects/pyclustertend/badge/?version=latest)](https://pyclustertend.readthedocs.io/en/latest/?badge=latest) [![Downloads](https://pepy.tech/badge/pyclustertend)](https://pepy.tech/project/pyclustertend) [![Downloads](https://pepy.tech/badge/pyclustertend/month)](https://pepy.tech/project/pyclustertend/month) 
        
        
        
        ## 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  :
        
        - [x] Hopkins Statistics 
        - [x] VAT
        - [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 Metric : 
        
        
        ```python
            >>>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
        ```
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Description-Content-Type: text/markdown
