https://github.com/tschechlovdev/kmeans_mnist
Demonstration of using k-Means to cluster images of handwritten digits (MNIST dataset). Source Code for corresponding article on Medium.
https://github.com/tschechlovdev/kmeans_mnist
clustering image-dataset python scikit-learn
Last synced: 3 months ago
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Demonstration of using k-Means to cluster images of handwritten digits (MNIST dataset). Source Code for corresponding article on Medium.
- Host: GitHub
- URL: https://github.com/tschechlovdev/kmeans_mnist
- Owner: tschechlovdev
- Created: 2024-03-27T07:13:53.000Z (almost 2 years ago)
- Default Branch: main
- Last Pushed: 2024-03-27T11:02:14.000Z (almost 2 years ago)
- Last Synced: 2025-02-15T01:38:21.939Z (11 months ago)
- Topics: clustering, image-dataset, python, scikit-learn
- Language: Jupyter Notebook
- Homepage: https://medium.com/@tschechd/k-means-clustering-on-image-data-using-the-mnist-dataset-8101fcc650eb
- Size: 124 KB
- Stars: 3
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: Readme.md
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README
# Applying k-Means on the Image Dataset MNIST
This repository showcases how to apply k-Means on the image dataset MNIST.
In particular, how to load the data, apply k-Means, visualize the results, and how to evaluate it.
You can find the corresponding post on Medium [here](https://medium.com/@tschechd/applying-understanding-k-means-on-image-data-using-the-mnist-dataset-8101fcc650eb).
## Installation
The notebook is written in Python 3.10. I suggest to install [Anaconda](https://docs.anaconda.com/).
To install the same environment you can simply run ``conda env create -f environment.yml``.
Alternatively, you can lookup the dependencies in the environment.yml file and install them, e.g., using pip.
Then just run ``jupyter-lab`` and open the ``kmeans_MNIST.ipynb`` notebook.