https://github.com/navefr/ExKMC
Expanding Explainable K-Means Clustering
https://github.com/navefr/ExKMC
Last synced: 5 months ago
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Expanding Explainable K-Means Clustering
- Host: GitHub
- URL: https://github.com/navefr/ExKMC
- Owner: navefr
- License: mit
- Created: 2020-05-11T05:33:13.000Z (almost 5 years ago)
- Default Branch: master
- Last Pushed: 2023-01-18T17:28:09.000Z (about 2 years ago)
- Last Synced: 2024-05-14T11:10:48.179Z (11 months ago)
- Language: Jupyter Notebook
- Homepage:
- Size: 1.55 MB
- Stars: 85
- Watchers: 3
- Forks: 12
- Open Issues: 3
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
- Awesome-explainable-AI - https://github.com/navefr/ExKMC
README
# ExKMC
This repository is the official implementation of [ExKMC: Expanding Explainable k-Means Clustering](https://arxiv.org/pdf/2006.02399.pdf).
We study algorithms for k-means clustering, focusing on a trade-off between explainability and accuracy.
We partition a dataset into k clusters via a small decision tree.
This enables us to explain each cluster assignment by a short sequence of single-feature thresholds.
While larger trees produce more accurate clusterings, they also require more complex explanations.
To allow flexibility, we develop a new explainable k-means clustering algorithm, ExKMC, that takes an additional parameter k' ≥ k and outputs a decision tree with k' leaves.
We use a new surrogate cost to efficiently expand the tree and to label the leaves with one of k clusters.
We prove that as k' increases, the surrogate cost is non-increasing, and hence, we trade explainability for accuracy.
## Installation
The package is on [PyPI](https://pypi.org/project/ExKMC/). Simply run:
```
pip install ExKMC
```
## Usage
```python
from ExKMC.Tree import Tree
from sklearn.datasets import make_blobs
# Create dataset
n = 100
d = 10
k = 3
X, _ = make_blobs(n, d, k, cluster_std=3.0)
# Initialize tree with up to 6 leaves, predicting 3 clusters
tree = Tree(k=k, max_leaves=2*k)
# Construct the tree, and return cluster labels
prediction = tree.fit_predict(X)
# Tree plot saved to filename
tree.plot('filename')
```
## Notebooks
Usage examples:
* [Expand tree for Gaussians](notebooks/Example.ipynb)
* [Expand tree for text data](notebooks/Newsgroups%20example.ipynb)
## Citation
If you use ExKMC in your research we would appreciate a citation to the appropriate paper(s):
* For IMM base tree you can read our [ICML 2020 paper](https://arxiv.org/pdf/2002.12538.pdf).
```bash
@article{dasgupta2020explainable,
title={Explainable $k$-Means and $k$-Medians Clustering},
author={Dasgupta, Sanjoy and Frost, Nave and Moshkovitz, Michal and Rashtchian, Cyrus},
journal={arXiv preprint arXiv:2002.12538},
year={2020}
}
```
* For ExKMC expansion you can read our [paper](https://arxiv.org/pdf/2006.02399.pdf).
```bash
@article{frost2020exkmc,
title={ExKMC: Expanding Explainable $k$-Means Clustering},
author={Frost, Nave and Moshkovitz, Michal and Rashtchian, Cyrus},
journal={arXiv preprint arXiv:2006.02399},
year={2020}
}
```
## Contact
* [Nave Frost](mailto:[email protected])
* [Michal Moshkovitz](https://sites.google.com/view/michal-moshkovitz)
* [Cyrus Rashtchian](https://sites.google.com/site/cyrusrashtchian/)