Metadata-Version: 2.1
Name: st_dbscan
Version: 0.2.0
Summary: Spatial-temporal DBSCAN
Home-page: https://github.com/eren-ck/st_dbscan
Author: Eren Cakmak
Author-email: 22754816+eren-ck@users.noreply.github.com
License: License :: OSI Approved :: MIT License
Platform: any
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Science/Research
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Topic :: Software Development
Classifier: Topic :: Scientific/Engineering
Classifier: Operating System :: Microsoft :: Windows
Classifier: Operating System :: Unix
Classifier: Operating System :: MacOS
Requires-Python: >=3.6
Description-Content-Type: text/markdown; charset=UTF-8
Provides-Extra: testing
License-File: LICENSE.txt

# ST-DBSCAN

**Simple and effective method for spatial-temporal clustering**

*st_dbscan* is an open-source software package for the spatial-temporal clustering of movement data:

- Implemnted using `numpy` and `sklearn`
- Scales to memory - using chuncking sparse matrices and the `st_dbscan.fit_frame_split`

## Installation
The easiest way to install *st_dbscan* is by using `pip` :

    pip install st-dbscan

## How to use

```python
from st_dbscan import ST_DBSCAN

st_dbscan = ST_DBSCAN(eps1 = 0.05, eps2 = 10, min_samples = 5)
st_dbscan.fit(data)

```

- __Demo Notebook:__ the following noteboook shows a demo of common features in this package -
[see Jupyter Notebook](/demo/demo.ipynb)

## Description

A package to perform the ST_DBSCAN clustering. If you use the package, please consider citing the following benchmark paper:

```bibtex
@inproceedings{cakmak2021spatio,
        author = {Cakmak, Eren and Plank, Manuel and Calovi, Daniel S. and Jordan, Alex and Keim, Daniel},
        title = {Spatio-Temporal Clustering Benchmark for Collective Animal Behavior},
        year = {2021},
        isbn = {9781450391221},
        publisher = {Association for Computing Machinery},
        address = {New York, NY, USA},
        url = {https://doi.org/10.1145/3486637.3489487},
        doi = {10.1145/3486637.3489487},
        booktitle = {Proceedings of the 1st ACM SIGSPATIAL International Workshop on Animal Movement Ecology and Human Mobility},
        pages = {5–8},
        numpages = {4},
        location = {Beijing, China},
        series = {HANIMOB '21}
}
```

## License
Released under MIT License. See the [LICENSE](LICENSE) file for details.
The package was developed by Eren Cakmak from the [Data Analysis and Visualization Group](https://www.vis.uni-konstanz.de/) and the [Department of Collective Behaviour](http://collectivebehaviour.com) at the University Konstanz funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany's Excellence Strategy – EXC 2117 – 422037984“

