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K-means Clustering Visualization Project

This project implements a K-means clustering algorithm with data visualization using Matplotlib and SciPy. The project includes the following key components:

Components

  1. Data Generation:

    • The generate_data function creates random data points with specified features and value ranges.

  2. Elbow Method:

    • The elbow_method function determines the optimal number of clusters by plotting the cost/inertia for different values of ( k ).

  3. K-means Clustering:

    • The kmeans function performs the K-means clustering algorithm, iteratively updating centroids and assigning data points to clusters until convergence or a maximum number of iterations is reached.

  4. Visualization:

    • The animate_kmeans function visualizes the clustering process by plotting the data points, centroids, and cluster boundaries at each iteration, saving the plots as images before updating.

  5. Execution:

    • The script generates random data, runs the Elbow method to determine the optimal number of clusters, performs K-means clustering, and displays the clustering process using Matplotlib.

Files

  • kmeans_visualizing_without_boundary.py: Implements the K-means clustering algorithm with data visualization.
  • kmeans_visualizing_with_boundary.py: Implements the K-means clustering algorithm with data visualization and cluster boundary.

Usage

  1. Generate Data:

    • Use the generate_data function to create random data points.

  2. Determine Optimal Clusters:

    • Use the elbow_method function to determine the optimal number of clusters.

  3. Run K-means Clustering:

    • Use the kmeans function to perform the clustering.

  4. Visualize Clustering Process:

    • Use the animate_kmeans function to visualize and save the clustering process.

Requirements

  • Python 3.x
  • Matplotlib
  • SciPy

Installation

Install the required packages using pip:

pip install matplotlib scipy

Running the Project

  1. Generate random data.
  2. Determine the optimal number of clusters using the Elbow method.
  3. Perform K-means clustering.
  4. Visualize the clustering process.
python3 kmean_visualizing_without_boundary.py

python3 kmean_visualilzing_with_boundary.py

License

This project is licensed under the MIT License.