{"id":14958369,"url":"https://github.com/lachhebo/pyclustertend","last_synced_at":"2025-10-24T14:31:55.273Z","repository":{"id":34895558,"uuid":"187477036","full_name":"lachhebo/pyclustertend","owner":"lachhebo","description":"A python package to assess cluster tendency","archived":false,"fork":false,"pushed_at":"2022-12-27T17:39:21.000Z","size":6782,"stargazers_count":46,"open_issues_count":7,"forks_count":8,"subscribers_count":3,"default_branch":"master","last_synced_at":"2024-10-29T23:33:37.437Z","etag":null,"topics":["cluster-analysis","cluster-tendency","clustering","clustertendency","data-science","hopkins","ivat","machine-learning","python","scikit-learn","statistics","vat","visual-assessment-cluster-tendency"],"latest_commit_sha":null,"homepage":"https://pyclustertend.readthedocs.io/en/master/index.html","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"bsd-3-clause","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/lachhebo.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null}},"created_at":"2019-05-19T12:58:23.000Z","updated_at":"2024-09-17T19:08:50.000Z","dependencies_parsed_at":"2023-01-15T10:01:47.619Z","dependency_job_id":null,"html_url":"https://github.com/lachhebo/pyclustertend","commit_stats":null,"previous_names":[],"tags_count":5,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/lachhebo%2Fpyclustertend","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/lachhebo%2Fpyclustertend/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/lachhebo%2Fpyclustertend/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/lachhebo%2Fpyclustertend/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/lachhebo","download_url":"https://codeload.github.com/lachhebo/pyclustertend/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":237990581,"owners_count":19398453,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["cluster-analysis","cluster-tendency","clustering","clustertendency","data-science","hopkins","ivat","machine-learning","python","scikit-learn","statistics","vat","visual-assessment-cluster-tendency"],"created_at":"2024-09-24T13:16:53.213Z","updated_at":"2025-10-24T14:31:51.512Z","avatar_url":"https://github.com/lachhebo.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"# pyclustertend\n\n\n\n\n\n\n[![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=master)](https://pyclustertend.readthedocs.io/en/master/) [![Downloads](https://pepy.tech/badge/pyclustertend)](https://pepy.tech/project/pyclustertend) [![codecov](https://codecov.io/gh/lachhebo/pyclustertend/branch/master/graph/badge.svg)](https://codecov.io/gh/lachhebo/pyclustertend)\n[![DOI](https://zenodo.org/badge/187477036.svg)](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    \u003e\u003e\u003efrom sklearn import datasets\n    \u003e\u003e\u003efrom pyclustertend import hopkins\n    \u003e\u003e\u003efrom sklearn.preprocessing import scale\n    \u003e\u003e\u003eX = scale(datasets.load_iris().data)\n    \u003e\u003e\u003ehopkins(X,150)\n    0.18950453452838564\n```\n\n### Example VAT\n\n```python\n    \u003e\u003e\u003efrom sklearn import datasets\n    \u003e\u003e\u003efrom pyclustertend import vat\n    \u003e\u003e\u003efrom sklearn.preprocessing import scale\n    \u003e\u003e\u003eX = scale(datasets.load_iris().data)\n    \u003e\u003e\u003evat(X)\n```\n\n\u003cimg height=\"350\" src=\"https://raw.githubusercontent.com/lachhebo/pyclustertend/screenshots/vat.png\" /\u003e\n\n### Example iVat\n\n```python\n    \u003e\u003e\u003efrom sklearn import datasets\n    \u003e\u003e\u003efrom pyclustertend import ivat\n    \u003e\u003e\u003efrom sklearn.preprocessing import scale\n    \u003e\u003e\u003eX = scale(datasets.load_iris().data)\n    \u003e\u003e\u003eivat(X)\n```\n\n\u003cimg height=\"350\" src=\"https://raw.githubusercontent.com/lachhebo/pyclustertend/screenshots/ivat.png\" /\u003e\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","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Flachhebo%2Fpyclustertend","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Flachhebo%2Fpyclustertend","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Flachhebo%2Fpyclustertend/lists"}