Skip to main content

Divisive iK-means algorithm implementation

Project description

CodeFactor BCH compliance Maintainability Documentation Status

divik

Python implementation of Divisive iK-means (DiviK) algorithm.

Tools within this package

This section will be further developed soon.

  1. divik - runs DiviK in GAP-only scenario
  2. dunn-divik - runs DiviK in GAP & Dunn scenario
  3. kmeans - runs K-means with GAP statistic
  4. linkage - runs agglomerative clustering
  5. inspect - visualizes DiviK result
  6. visualize - generates .png file with visualization of clusters for 2D maps
  7. spectral - generates spectral embedding of a dataset

Installation

Docker

The recommended way to use this software is through Docker. This is the most convenient way, if you want to use divik application.

To install latest stable version use:

docker pull gmrukwa/divik

To install specific version, you can specify it in the command, e.g.:

docker pull gmrukwa/divik:2.3.20

Python package

Prerequisites for installation of base package:

  • Python 3.6 / 3.7 / 3.8
  • compiler capable of compiling the native C code

Having prerequisites installed, one can install latest base version of the package:

pip install divik

or any stable tagged version, e.g.:

pip install divik==2.3.20

If you want to have compatibility with gin-config, you can install necessary extras with:

pip install divik[gin]

Note: Remember about \ before [ and ] in zsh shell.

References

This software is part of contribution made by Data Mining Group of Silesian University of Technology, rest of which is published here.

Release history Release notifications

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Files for divik, version 2.3.20
Filename, size File type Python version Upload date Hashes
Filename, size divik-2.3.20-cp36-cp36m-macosx_10_13_x86_64.whl (121.5 kB) File type Wheel Python version cp36 Upload date Hashes View hashes
Filename, size divik-2.3.20-cp36-cp36m-manylinux1_x86_64.whl (160.8 kB) File type Wheel Python version cp36 Upload date Hashes View hashes
Filename, size divik-2.3.20-cp36-cp36m-win_amd64.whl (131.2 kB) File type Wheel Python version cp36 Upload date Hashes View hashes
Filename, size divik-2.3.20-cp37-cp37m-macosx_10_13_x86_64.whl (121.5 kB) File type Wheel Python version cp37 Upload date Hashes View hashes
Filename, size divik-2.3.20-cp37-cp37m-manylinux1_x86_64.whl (160.8 kB) File type Wheel Python version cp37 Upload date Hashes View hashes
Filename, size divik-2.3.20-cp37-cp37m-win_amd64.whl (131.2 kB) File type Wheel Python version cp37 Upload date Hashes View hashes
Filename, size divik-2.3.20-cp38-cp38-macosx_10_13_x86_64.whl (121.6 kB) File type Wheel Python version cp38 Upload date Hashes View hashes
Filename, size divik-2.3.20-cp38-cp38-manylinux1_x86_64.whl (161.3 kB) File type Wheel Python version cp38 Upload date Hashes View hashes
Filename, size divik-2.3.20-cp38-cp38-win_amd64.whl (131.2 kB) File type Wheel Python version cp38 Upload date Hashes View hashes
Filename, size divik-2.3.20.tar.gz (76.9 kB) File type Source Python version None Upload date Hashes View hashes

Supported by

Elastic Elastic Search Pingdom Pingdom Monitoring Google Google BigQuery Sentry Sentry Error logging AWS AWS Cloud computing DataDog DataDog Monitoring Fastly Fastly CDN DigiCert DigiCert EV certificate StatusPage StatusPage Status page