https://github.com/khaledashrafh/k-means-clustering
This program implements the K-means clustering algorithm using OpenMP APIs. The K-means algorithm is a popular method of vector quantization that aims to partition n observations into k clusters. Each observation is assigned to the cluster with the nearest mean, serving as a prototype of the cluster.
https://github.com/khaledashrafh/k-means-clustering
euclidean-distances gcc-complier high-performance-computing k-means-algorithm k-means-clustering open-mp openmp parallel-processing
Last synced: 30 days ago
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This program implements the K-means clustering algorithm using OpenMP APIs. The K-means algorithm is a popular method of vector quantization that aims to partition n observations into k clusters. Each observation is assigned to the cluster with the nearest mean, serving as a prototype of the cluster.
- Host: GitHub
- URL: https://github.com/khaledashrafh/k-means-clustering
- Owner: KhaledAshrafH
- License: mit
- Created: 2022-09-11T06:47:11.000Z (over 2 years ago)
- Default Branch: main
- Last Pushed: 2023-08-23T13:59:25.000Z (over 1 year ago)
- Last Synced: 2025-02-02T07:28:17.894Z (3 months ago)
- Topics: euclidean-distances, gcc-complier, high-performance-computing, k-means-algorithm, k-means-clustering, open-mp, openmp, parallel-processing
- Language: C
- Homepage:
- Size: 55.7 KB
- Stars: 3
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE.md
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README
# K-Means Clustering Algorithm
This program implements the K-means clustering algorithm using OpenMP APIs. The K-means algorithm is a popular method of vector quantization that aims to partition n observations into k clusters. Each observation is assigned to the cluster with the nearest mean, serving as a prototype of the cluster.
## Application and Use Cases
The K-means algorithm finds applications in various domains, including:
- **Market Segmentation**: Identifying distinct groups of customers based on their purchasing behavior and preferences.
- **Document Clustering**: Grouping similar documents together based on their content to aid in information retrieval and text mining.
- **Image Segmentation**: Dividing an image into meaningful regions or objects based on color, texture, or other visual features.
- **Image Compression**: Reducing the size of an image by representing it using a smaller number of cluster centroids.
## Algorithm Overview
The K-means algorithm follows these steps:
1. **Specify the number of clusters, K**: Determine the desired number of clusters to partition the data.
2. **Initialize centroids**: Randomly select K data points as the initial cluster centroids.
3. **Iterate until convergence**:
- **Assign data points to clusters**: Calculate the distance between each data point and the cluster centroids. Assign each data point to the cluster with the closest centroid.
- **Update cluster centroids**: Recalculate the centroids by taking the average of all data points assigned to each cluster.
- **Check for convergence**: Repeat the above steps until the centroids no longer change significantly or a maximum number of iterations is reached.
## Distance Calculation
The Euclidean distance formula is used to calculate the distance between a data point and its closest centroid. Given two points (x1, y1) and (x2, y2), the Euclidean distance is computed as:
```
distance = sqrt((x1 - x2)^2 + (y1 - y2)^2)
```
## Requirements
To run the program, the following requirements should be met:
1. **Known number of data points**: The number of data points to be clustered should be known in advance.
2. **Data points in 2 dimensions**: The data points should be represented as (x, y) coordinates in a 2-dimensional space.
3. **OpenMP APIs only**: The program should use only OpenMP APIs for parallel computing and not MPI APIs.
4. **Read data points from a file**: The program should read the data points from a file named "points.txt" within the C script. The format of the file should have each data point on a separate line.
5. **Program output**: The program should display the clusters and their corresponding data points in the following format:
```
Cluster 1:
(x1, y1)
(x2, y2)
...
```
6. **Number of threads**: The number of threads used should be equal to the number of clusters specified when running the program.
7. **Stopping criteria**: The program should allow flexibility in choosing the maximum number of iterations or a threshold for the centroid difference error to determine convergence.
## Usage
Follow these steps to use the program:
1. **Compile the program**: Use a C compiler that supports OpenMP to compile the program.
2. **Prepare the data file**: Create a file named "points.txt" and populate it with the data points to be clustered. Each data point should be on a separate line, with the x and y coordinates separated by a space or comma.
3. **Run the program**: Execute the compiled program.
4. **Observe the output**: The program will display the clusters and their corresponding data points in the console.
Note: Ensure that the number of clusters specified when running the program matches the desired number of clusters for partitioning the data.
## Contributing
Contributions are welcome! If you find any issues or have suggestions for improvement, please open an issue or submit a pull request.
## Authors
- [Khaled Ashraf Hanafy Mahmoud - 20190186](https://github.com/KhaledAshrafH).
- [Ahmed Sayed Hassan Youssef - 20190034](https://github.com/AhmedSayed117).
- [Samah Moustafa Hussien Mahmoud - 20190248](https://github.com/Samah-20190248).
## License
This program is licensed under the [MIT License](LICENSE.md).