https://github.com/hlsxx/k-means-rust
The aim of this project is to implement the k-means algorithm using Rust-lang. The source code includes a parallel implementation in Rayon.
https://github.com/hlsxx/k-means-rust
k-means k-means-clustering k-means-rust kmeans kmeans-algorithm kmeans-clustering kmeans-clustering-algorithm kmeans-rust parallel-programming rayon rust rust-lang rust-parallel
Last synced: 2 months ago
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The aim of this project is to implement the k-means algorithm using Rust-lang. The source code includes a parallel implementation in Rayon.
- Host: GitHub
- URL: https://github.com/hlsxx/k-means-rust
- Owner: hlsxx
- Created: 2023-04-22T15:54:26.000Z (about 2 years ago)
- Default Branch: master
- Last Pushed: 2023-09-19T19:30:30.000Z (over 1 year ago)
- Last Synced: 2025-01-03T22:15:25.775Z (4 months ago)
- Topics: k-means, k-means-clustering, k-means-rust, kmeans, kmeans-algorithm, kmeans-clustering, kmeans-clustering-algorithm, kmeans-rust, parallel-programming, rayon, rust, rust-lang, rust-parallel
- Language: Rust
- Homepage:
- Size: 1.9 MB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# ✨ k-means-rust
The aim of this project is to implement the k-means algorithm using Rust-lang. The source code includes a parallel implementation in [Rayon](https://github.com/rayon-rs/rayon).
Here are some key characteristics of the K-means algorithm:
1. Initialization: The algorithm starts by randomly selecting K cluster centroids from the dataset.
2. Assignment: Each data point is then assigned to the nearest centroid based on the Euclidean distance metric.
3. Update: The centroids of each cluster are updated by taking the mean of all data points assigned to that cluster.
4. Repeat: Steps 2 and 3 are repeated until convergence, that is, until the assignment of data points to clusters no longer changes.
5. Optimal K: The choice of K, the number of clusters, can significantly impact the clustering results, and it is often determined using heuristics or optimization techniques.
## 🚀 Generate points
If you want to create more or fewer points, you can use the "points_generator.rs" file located in the "bin" folder. Running the command below will generate points and store them in a "points.txt" file within the "inputs" folder.
```sh
cargo run --bin points_generator
```
## ✨ Usage
The "examples" folder contains multiple implementations of the k-means algorithm, each of which differs from the others in some way.
```sh
cargon run --example parallel-iterations-2
```
## 🏁 Result
The program will generate a plot and store it in the "outputs" folder.
