# Amazon DenseClus
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DenseClus is a Python module for clustering mixed type data using [UMAP](https://github.com/lmcinnes/umap) and [HDBSCAN](https://github.com/scikit-learn-contrib/hdbscan). Allowing for both categorical and numerical data, DenseClus makes it possible to incorporate all features in clustering.
## Installation
```bash
python3 -m pip install Amazon-DenseClus
```
## Usage
DenseClus requires a Panda's dataframe as input with both numerical and categorical columns.
All preprocessing and extraction are done under the hood, just call fit and then retrieve the clusters!
```python
from denseclus import DenseClus
clf = DenseClus(
umap_combine_method="intersection_union_mapper",
)
clf.fit(df)
print(clf.score())
```
## Examples
A hands-on example with an overview of how to use is currently available in the form of a [Jupyter Notebook](/notebooks/DenseClus%20Example%20NB.ipynb).
## References
```bibtex
@article{mcinnes2018umap-software,
title={UMAP: Uniform Manifold Approximation and Projection},
author={McInnes, Leland and Healy, John and Saul, Nathaniel and Grossberger, Lukas},
journal={The Journal of Open Source Software},
volume={3},
number={29},
pages={861},
year={2018}
}
```
```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}
}
```
