[](https://badge.fury.io/py/pyflasc)
[](https://github.com/vda-lab/pyflasc/actions/workflows/Wheels.yml)
# FLASC: Flare-Sensitive Clustering
FLASC - Flare-Sensitive Clustering, adds an efficient post-processing step to
the [HDBSCAN\*](https://github.com/scikit-learn-contrib/hdbscan) density-based
clustering algorithm to detect branching structures within clusters.
The algorithm adds two parameters that may need tuning with respect to
HDBSCAN\*, but both are intuitive to tune: minimum branch size and branch
selection strategy.
## How to use FLASC
The FLASC package is closely based on the HDBSCAN* package and supports the same
API, except sparse inputs, which are not supported yet.
```python
from flasc import FLASC
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
data = np.load('./notebooks/data/flared_clusterable_data.npy')
clusterer = FLASC(min_cluster_size=15)
clusterer.fit(data)
colors = sns.color_palette('tab10', 10)
point_colors = [
sns.desaturate(colors[l], p)
for l, p in zip(clusterer.labels_, clusterer.probabilities_)
]
plt.scatter(data[:, 0], data[:, 1], 2, point_colors, alpha=0.5)
plt.axis('off')
plt.show()
```
## Example Notebooks
A notebook demonstrating how the algorithm works is available at [How FLASC
Works](https://nbviewer.org/github/vda-lab/pyflasc/blob/master/notebooks/How%20FLASC%20Works.ipynb).
The other notebooks demonstrate the algorithm on several data sets and contain
the analyses presented in our paper.
## Installing
Binary wheels are available on PyPI. Presuming you have an up-to-date pip:
```bash
pip install pyflasc
```
For a manual install of the latest code directly from GitHub:
```bash
pip install --upgrade git+https://github.com/vda-lab/pyflasc.git#egg=pyflasc
```
Alternatively download the package, install requirements, and manually run the
installer:
```bash
wget https://github.com/vda-lab/pyflasc/archive/main.zip
unzip main.zip
rm main.zip
cd flasc-main
pip install -t .
```
## Citing
A scientific publication of this algorithm is available on Arxiv:
```bibtex
@misc{bot2023flasc,
title={FLASC: A Flare-Sensitive Clustering Algorithm: Extending HDBSCAN* for Detecting Branches in Clusters},
author={D. M. Bot and J. Peeters and J. Liesenborgs and J. Aerts},
year={2023},
eprint={2311.15887},
archivePrefix={arXiv},
primaryClass={cs.LG}
}
```
This FLASC algorithm and software package is very closely related to McInnes et
al.'s HDBSCAN\* software package. If you wish to cite the HDBSCAN\* package in a
scientific publication, please use their [Journal of Open Source Software
article](http://joss.theoj.org/papers/10.21105/joss.00205).
L. McInnes, J. Healy, S. Astels, *hdbscan: Hierarchical density based clustering*
In: Journal of Open Source Software, The Open Journal, volume 2, number 11.
2017
```bibtex
@article{mcinnes2017hdbscan,
title={hdbscan: Hierarchical density based clustering},
author={McInnes, Leland and Healy, John and Astels, Steve},
journal={The Journal of Open Source Software},
volume={2},
number={11},
pages={205},
year={2017}
}
```
To reference their high performance algorithm please cite their paper in ICDMW
2017 proceedings.
McInnes L, Healy J. *Accelerated Hierarchical Density Based Clustering*
In: 2017 IEEE International Conference on Data Mining Workshops (ICDMW), IEEE, pp 33-42.
2017
```bibtex
@inproceedings{mcinnes2017accelerated,
title={Accelerated Hierarchical Density Based Clustering},
author={McInnes, Leland and Healy, John},
booktitle={Data Mining Workshops (ICDMW), 2017 IEEE International Conference on},
pages={33--42},
year={2017},
organization={IEEE}
}
```
## Licensing
The FLASC package has a 3-Clause BSD license.
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"description": "[](https://badge.fury.io/py/pyflasc)\n[](https://github.com/vda-lab/pyflasc/actions/workflows/Wheels.yml)\n\n# FLASC: Flare-Sensitive Clustering\n\nFLASC - Flare-Sensitive Clustering, adds an efficient post-processing step to\nthe [HDBSCAN\\*](https://github.com/scikit-learn-contrib/hdbscan) density-based\nclustering algorithm to detect branching structures within clusters.\n\nThe algorithm adds two parameters that may need tuning with respect to\nHDBSCAN\\*, but both are intuitive to tune: minimum branch size and branch\nselection strategy.\n\n## How to use FLASC\n\nThe FLASC package is closely based on the HDBSCAN* package and supports the same\nAPI, except sparse inputs, which are not supported yet.\n\n```python\nfrom flasc import FLASC\nimport numpy as np\nimport seaborn as sns\nimport matplotlib.pyplot as plt\n\ndata = np.load('./notebooks/data/flared_clusterable_data.npy')\nclusterer = FLASC(min_cluster_size=15)\nclusterer.fit(data)\ncolors = sns.color_palette('tab10', 10)\npoint_colors = [\n sns.desaturate(colors[l], p)\n for l, p in zip(clusterer.labels_, clusterer.probabilities_)\n]\nplt.scatter(data[:, 0], data[:, 1], 2, point_colors, alpha=0.5)\nplt.axis('off')\nplt.show()\n```\n\n\n\n## Example Notebooks\n\nA notebook demonstrating how the algorithm works is available at [How FLASC\nWorks](https://nbviewer.org/github/vda-lab/pyflasc/blob/master/notebooks/How%20FLASC%20Works.ipynb).\nThe other notebooks demonstrate the algorithm on several data sets and contain\nthe analyses presented in our paper.\n\n## Installing\n\nBinary wheels are available on PyPI. Presuming you have an up-to-date pip:\n\n```bash\npip install pyflasc\n```\nFor a manual install of the latest code directly from GitHub:\n\n```bash\npip install --upgrade git+https://github.com/vda-lab/pyflasc.git#egg=pyflasc\n```\n\nAlternatively download the package, install requirements, and manually run the\ninstaller:\n\n```bash\nwget https://github.com/vda-lab/pyflasc/archive/main.zip\nunzip main.zip\nrm main.zip\ncd flasc-main\n\npip install -t .\n```\n\n## Citing\n\nA scientific publication of this algorithm is available on Arxiv:\n\n```bibtex\n@misc{bot2023flasc,\n title={FLASC: A Flare-Sensitive Clustering Algorithm: Extending HDBSCAN* for Detecting Branches in Clusters}, \n author={D. M. Bot and J. Peeters and J. Liesenborgs and J. Aerts},\n year={2023},\n eprint={2311.15887},\n archivePrefix={arXiv},\n primaryClass={cs.LG}\n}\n```\n\nThis FLASC algorithm and software package is very closely related to McInnes et\nal.'s HDBSCAN\\* software package. If you wish to cite the HDBSCAN\\* package in a\nscientific publication, please use their [Journal of Open Source Software\narticle](http://joss.theoj.org/papers/10.21105/joss.00205).\n\n L. McInnes, J. Healy, S. Astels, *hdbscan: Hierarchical density based clustering*\n In: Journal of Open Source Software, The Open Journal, volume 2, number 11.\n 2017\n\n```bibtex\n@article{mcinnes2017hdbscan,\n title={hdbscan: Hierarchical density based clustering},\n author={McInnes, Leland and Healy, John and Astels, Steve},\n journal={The Journal of Open Source Software},\n volume={2},\n number={11},\n pages={205},\n year={2017}\n}\n```\n\nTo reference their high performance algorithm please cite their paper in ICDMW\n2017 proceedings.\n\n McInnes L, Healy J. *Accelerated Hierarchical Density Based Clustering*\n In: 2017 IEEE International Conference on Data Mining Workshops (ICDMW), IEEE, pp 33-42.\n 2017\n\n```bibtex\n@inproceedings{mcinnes2017accelerated,\n title={Accelerated Hierarchical Density Based Clustering},\n author={McInnes, Leland and Healy, John},\n booktitle={Data Mining Workshops (ICDMW), 2017 IEEE International Conference on},\n pages={33--42},\n year={2017},\n organization={IEEE}\n}\n```\n\n## Licensing\n\nThe FLASC package has a 3-Clause BSD license.\n",
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