https://github.com/saadarazzaq/cluster-analysis-elbow-method
implements the elbow method to determine the optimal number of clusters (k) for a given dataset using the K-means clustering algorithm.
https://github.com/saadarazzaq/cluster-analysis-elbow-method
elbow-method euclidean-distances kmeans-algorithm python unsupervised-learning
Last synced: about 1 month ago
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implements the elbow method to determine the optimal number of clusters (k) for a given dataset using the K-means clustering algorithm.
- Host: GitHub
- URL: https://github.com/saadarazzaq/cluster-analysis-elbow-method
- Owner: SaadARazzaq
- Created: 2023-07-24T08:20:26.000Z (almost 2 years ago)
- Default Branch: main
- Last Pushed: 2023-07-24T08:25:00.000Z (almost 2 years ago)
- Last Synced: 2025-01-23T16:14:33.205Z (3 months ago)
- Topics: elbow-method, euclidean-distances, kmeans-algorithm, python, unsupervised-learning
- Language: Python
- Homepage:
- Size: 3.91 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Cluster-Analysis-Elbow-Method
## Introduction
This repository implements the elbow method to determine the optimal number of clusters (k) for a given dataset using the K-means clustering algorithm. The elbow method is a common technique to find the ideal value of k by plotting the total within-cluster sum of squares (WCSS) against different k values.
## Dataset
The dataset used for this project is available in the "Data.csv" file.
## Code Explanation
The code is written in Python and uses the following functions:
- `euclidean_distance(a, b)`: Calculates the Euclidean distance between two points `a` and `b`.
- `k_means(data, k)`: Implements the K-means clustering algorithm with `k` clusters.
- `total_within_cluster_sum_of_squares(data, centroids, assignments)`: Calculates the total within-cluster sum of squares (WCSS) for a given set of data points, centroids, and cluster assignments.
- `elbow_method(data, max_k)`: Uses the K-means algorithm to find WCSS for different values of `k` and returns a list of WCSS values.
- `visualize_elbow_method(wcss)`: Plots the WCSS values against different `k` values to visualize the elbow method.
## Usage
1. Clone the repository to your local machine.
2. Make sure you have Python and the required libraries installed (NumPy, Pandas, and Matplotlib).
3. Run the Python script `main.py`.
4. The script will output the optimal value of k and plot the elbow method graph.
## Screenshot
## Results
After running the code, the elbow method graph will be displayed showing the WCSS values for different k values. The optimal number of clusters (k) can be identified at the "elbow point" in the graph where the rate of WCSS reduction starts to slow down.
## Contributing
Contributions to this project are welcome. If you find any issues or have suggestions for improvements, please feel free to open a pull request or submit an issue.
## Contact
For any questions or inquiries, you can reach me at [email protected].