https://github.com/joeylemon/python-kmeans
an implementation of the K-means clustering algorithm in Python
https://github.com/joeylemon/python-kmeans
k-means-clustering matplotlib ml numpy python
Last synced: 30 days ago
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an implementation of the K-means clustering algorithm in Python
- Host: GitHub
- URL: https://github.com/joeylemon/python-kmeans
- Owner: joeylemon
- Created: 2021-09-30T14:50:08.000Z (over 3 years ago)
- Default Branch: master
- Last Pushed: 2021-10-06T00:33:08.000Z (over 3 years ago)
- Last Synced: 2025-01-19T20:47:04.303Z (about 1 month ago)
- Topics: k-means-clustering, matplotlib, ml, numpy, python
- Language: Python
- Homepage:
- Size: 1.49 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# python-kmeans
An implementation of the [K-means clustering](https://en.wikipedia.org/wiki/K-means_clustering) unsupervised machine learning algorithm used to reduce the number of colors required to represent an image.
## Motivation
As students in COSC425: Introduction to Machine Learning at the [University of Tennessee](https://utk.edu/), we were tasked with implementing the K-means algorithm from scratch in Python. We were then to use the algorithm to determine the best set of RGB colors to represent a given image. Finally, we had to analyze the performance of our algorithm by observing how quickly the clusters reached a centroid convergence and the distribution of pixels and their corresponding clusters. We performed our algorithms on images with K values of 4, 16, or 32. We set a max iteration count of 24, and we determined convergence with a max RGB value delta of 1.
## Analysis
For the image above of [Smokey](https://en.wikipedia.org/wiki/Smokey_(mascot)), the mascot of the University of Tennessee, we can plot how quickly a set of K values reach a cluster convergence. We can also observe the distribution of pixels and their corresponding clusters:
As a further step to understanding how our K-means algorithm reaches a conclusion, we can plot the image's RGB values in 3D and observe how the cluster centroids converge as the algorithm iterates. The below figure observes a separate run of the algorithm with K=4 on the image of Smokey:
## How to Run
To run the program on other images and K values, edit the main function of `kmeans.py` with the appropriate values:
```py
if __name__ == "__main__":
k_values = [
{"K": 4, "color": "blue", "xticks": [1, 2, 3, 4]},
{"K": 16, "color": "red", "xticks": [1, 8, 16]},
{"K": 32, "color": "green", "xticks": [1, 8, 16, 32]}
]
perform_comparison("images/baboon.jpeg", k_values)
perform_comparison("images/rocket.jpeg", k_values)
perform_comparison("images/smokey.jpeg", k_values)
perform_comparison("images/truck.jpeg", k_values)
```
Then, execute the script:
```sh
> python kmeans.py
```
The program is accompanied by a unit test to ensure the image reduction is working correctly. You can run the test with:
```sh
> python test.py
```