Kmeans

Kmeans is an R package providing an implementation of the K-Means clustering algorithm. The package includes additional features such as visualization of clustering results and performance benchmarking against the base R kmeans function.

Continuous Integration (CI)

This project uses GitHub Actions for Continuous Integration to ensure package quality and reproducibility. The CI pipeline includes:

• Installing package dependencies
• Building and testing vignettes
• Running R CMD check to verify package integrity

Status

Installation

To install the package from GitHub:

devtools::install_github("Cameron-zgl/625_Rpackage_kmeans/")

Features

1. Custom K-Means Implementation: Provides a modular implementation of the K-Means clustering algorithm via the my_kmeans function.
2. Cluster Visualization: Visualize clustering results in 2D space using plot_clusters.
3. Performance Benchmarking: Compare the performance of my_kmeans with the base R kmeans function using benchmark_kmeans.
4. C++ Integration: Leverages C++ for efficient computation of distances.

Example Usage

Clustering with my_kmeans

library(Kmeans)

# Generate example data
data <- matrix(rnorm(1000), ncol = 2)

# Perform clustering
result <- my_kmeans(data, centers = 3, nstart = 10)
print(result)

Visualizing Clustering Results

# Visualize the clustering results
plot_clusters(data, result$cluster, result$centers)

Performance Benchmarking

# Benchmark the performance of my_kmeans and base kmeans
benchmark <- benchmark_kmeans(data, centers = 3, nstart = 10)
print(benchmark)

Example Testing

After installation, you can test the package functionality using the examples provided above or your custom data. Below is a quick test:

data <- matrix(rnorm(500), ncol = 2)
result <- my_kmeans(data, centers = 2, nstart = 5)
plot_clusters(data, result$cluster, result$centers)

For more detailed,you can find references in documents such as vignettes. DEMO test output is in the folder doc.

Contribution

Contributions are welcome! If you find any issues or have suggestions for new features, feel free to open an issue or submit a pull request on GitHub.

Steps to Contribute:

1. Fork the repository.
2. Clone your fork locally.
3. Create a new branch for your feature or bug fix.
4. Commit your changes and push to your fork.
5. Submit a pull request describing your changes.

License

This project is licensed under the MIT License. See the LICENSE file for details.

Acknowledgments

• This package was developed as part of the Biostat 625 coursework at the University of Michigan.
• Special thanks to the instructors and teaching assistants for their guidance and support.