https://github.com/sergiosim/multicons

MultiCons (Multiple Consensuses) algorithm
https://github.com/sergiosim/multicons

clustering clustering-ensemble consensus-clustering frequent-closed-itemsets multicons multiple-consenuses unsupervised-learning

Last synced: 11 days ago
JSON representation

MultiCons (Multiple Consensuses) algorithm

• Host: GitHub
• URL: https://github.com/sergiosim/multicons
• Owner: SergioSim
• License: mit
• Created: 2021-11-14T10:39:03.000Z (almost 3 years ago)
• Default Branch: master
• Last Pushed: 2024-05-09T17:59:37.000Z (6 months ago)
• Last Synced: 2024-10-17T03:50:16.095Z (21 days ago)
• Topics: clustering, clustering-ensemble, consensus-clustering, frequent-closed-itemsets, multicons, multiple-consenuses, unsupervised-learning
• Language: Python
• Homepage: https://sergiosim.github.io/multicons/
• Size: 22.9 MB
• Stars: 3
• Watchers: 3
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • Changelog: CHANGELOG.md
  • License: LICENSE.md

Awesome Lists containing this project

README

        
# MultiCons

This python package provides an implementation of the MultiCons (Multiple Consensus)

algorithm.

MultiCons is a consensus clustering method that uses the frequent closed itemset mining

technique to find similarities in the base clustering solutions.

The implementation aims to follow the original description of the MultiCons method from

the references below.

## Installation

MultiCons is available on the Python Package Index (PyPI). It's installable using `pip`:

```bash

pip install multicons

```

## Documentation

To get started, check out some examples or look up the reference API, please visit our

[documentation page](https://sergiosim.github.io/multicons/).

## References

Atheer A. "A closed patterns-based approach to the consensus clustering problem".

Other [cs.OH]. Université Côte d’Azur, 2016. English. . 

Retrieved from [tel.archives-ouvertes.fr](https://tel.archives-ouvertes.fr/tel-01478626)

Atheer A., Pasquier N., Precioso F. "Using Closed Patterns to Solve the Consensus Clustering Problem".

International Journal of Software Engineering and Knowledge Engineering 2016 26:09n10, 1379-1397