Metadata-Version: 2.1
Name: kmedoids
Version: 0.1.5-dev
Classifier: License :: OSI Approved :: GNU General Public License v3 or later (GPLv3+)
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: Programming Language :: Python
Classifier: Programming Language :: Rust
Classifier: Operating System :: POSIX
Classifier: Operating System :: MacOS
Classifier: Operating System :: Microsoft :: Windows
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Software Development :: Libraries
Requires-Dist: numpy
Summary: k-Medoids clustering with the FasterPAM algorithm
Home-Page: https://github.com/kno10/python-kmedoids
Author: Erich Schubert <erich.schubert@tu-dortmund.de>
Author-Email: Erich Schubert <erich.schubert@tu-dortmund.de>
License: GPL-3.0-or-later
Description-Content-Type: text/markdown; charset=UTF-8; variant=GFM

# k-Medoids Clustering in Python with FasterPAM

This python package implements k-medoids clustering with PAM.
It can be used with arbitrary dissimilarites, as it requires a dissimilarity matrix as input.

For further details on the implemented algorithm FasterPAM, see:

> Erich Schubert, Peter J. Rousseeuw  
> **Fast and Eager k-Medoids Clustering:**  
> **O(k) Runtime Improvement of the PAM, CLARA, and CLARANS Algorithms**  
> Under review at Information Systems, Elsevier.  
> Preprint: <https://arxiv.org/abs/2008.05171>

an earlier (slower, and now obsolete) version was published as:

> Erich Schubert, Peter J. Rousseeuw:  
> **Faster k-Medoids Clustering: Improving the PAM, CLARA, and CLARANS Algorithms**  
> In: 12th International Conference on Similarity Search and Applications (SISAP 2019), 171-187.  
> <https://doi.org/10.1007/978-3-030-32047-8_16>  
> Preprint: <https://arxiv.org/abs/1810.05691>

This is a port of the original Java code from [ELKI](https://elki-project.github.io/) to Rust.
The [Rust version](https://github.com/kno10/rust-kmedoids) is then wrapped for use with Python.

If you use this code in scientific work, please cite above papers. Thank you.

## Example

```
import kmedoids
c = kmedoids.fasterpam(data, 5)
print("Loss is:", c.loss)
```

## Implemented Algorithms

* **FasterPAM** (Schubert and Rousseeuw, 2020, 2021)
* FastPAM1 (Schubert and Rousseeuw, 2019, 2021)
* PAM (Kaufman and Rousseeuw, 1987) with BUILD and SWAP
* Alternatig (k-means-style approach)

Note that the k-means style "alternating" algorithm yields rather poor result quality.

## License: GPL-3 or later

> This program is free software: you can redistribute it and/or modify
> it under the terms of the GNU General Public License as published by
> the Free Software Foundation, either version 3 of the License, or
> (at your option) any later version.
> 
> This program is distributed in the hope that it will be useful,
> but WITHOUT ANY WARRANTY; without even the implied warranty of
> MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
> GNU General Public License for more details.
> 
> You should have received a copy of the GNU General Public License
> along with this program.  If not, see <https://www.gnu.org/licenses/>.

## FAQ: Why GPL and not Apache/MIT/BSD?

Because copyleft software like Linux is what built the open-source community.

Tit for tat: you get to use my code, I get to use your code.

