Metadata-Version: 2.1
Name: st_clustering
Version: 0.0.4
Summary: Package containing various spatio-temporal clustering algorithms
Home-page: https://github.com/dbvis-ukon/spatio-temporal-clustering
Author: Eren Cakmak, Manuel Plank
Author-email: eren.cakmak@uni-konstanz.de
License: UNKNOWN
Description: # ST-CLUSTERING
        
        **st_clustering** is an open-source software package for spatial-temporal clustering:
        
        - Built on top of `sklearn`'s clustering algorithms
        - Scales to memory using chuncking. See the `st_fit_frame_split` method
        
        ## Installation
        The easiest way to install *st_clustering* is by using `pip` :
        
            pip install st_clustering
        
        ## How to use
        
        ```python
        import st_clustering as stc
        
        st_dbscan = stc.ST_DBSCAN(eps1 = 0.05, eps2 = 10, min_samples = 5)
        st_dbscan.st_fit(data)
        
        ```
        
        - __Demo Notebook:__ this [Jupyter Notebook](/demo/demo.ipynb) shows a demo of common features in this package.
        
        ## Description
        
        A package that implements a straightforward extension for various clustering algorithms to accomodate spatio-temporal data. 
        Available algorithms are:
        
        - ST DBSCAN
        - ST Agglomerative
        - ST OPTICS
        - ST Spectral Clustering
        - ST Affinity Propagation
        - ST HDBSCAN
        
        For more details please see original [paper](https://scibib.dbvis.de/uploadedFiles/Cakmak_ST_Clustering_Benchmark.pdf):
        
        ```
        Cakmak, E., Plank, M., Calovi, D. S., Jordan, A., & Keim, D. (2021). Spatio-temporal clustering benchmark for collective animal behavior. In 1st ACM SIGSPATIAL International Workshop on Animal Movement Ecology and Human Mobility (HANIMOB’21).
        ```
        
        ## License
        Released under MIT License. See the [LICENSE](LICENSE) file for details.
        
Platform: UNKNOWN
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
Provides-Extra: testing
