/K-Means-Clustering-Simulator

Primary LanguagePythonGNU General Public License v3.0GPL-3.0

K-Means Clustering Simulator App

This is a Dash web application for simulating and visualizing the K-Means clustering algorithm.
Github Link

Introduction

The K-Means algorithm is an unsupervised machine learning technique used for clustering similar data points into groups or clusters. It aims to partition a dataset into K clusters, where each data point belongs to the cluster with the nearest mean (centroid). This application provides an interactive environment to understand and visualize the K-Means algorithm.

Features

  • Dataset Generation: Choose different shapes of datasets such as blobs, moons, and circles with varying sizes and numbers of clusters.
  • Centroid Initialization: Select different methods for initializing centroids, including random initialization and K-Means++ initialization.
  • Step-by-Step Visualization: Observe the step-by-step execution of the K-Means algorithm, including the expectation step and maximization step.
  • Inertia Plot: Track the change in inertia (within-cluster sum of squares) over iterations to assess the convergence of the algorithm.
  • Silhouette Coefficient: Evaluate the quality of clustering using the silhouette coefficient, which measures the cohesion and separation of clusters.
  • Play and Pause Animation: Control the animation of the algorithm with play and pause buttons for better visualization.
  • Customizable Parameters: Adjust parameters such as the number of clusters, dataset size, and maximum iterations to explore different scenarios.

How to Run

To run the K-Means Clustering Simulator App locally, follow these steps:

  1. Clone Repository: Clone this repository to your local machine using Git:

    git clone https://github.com/yashmaniya0/K-Means-Clustering-Simulator

  2. Navigate to Directory: Navigate to the directory containing the cloned repository:

    cd K-Means-Clustering-Simulator

  3. Install Dependencies: Install the required Python dependencies using pip:

    pip install -r requirements.txt

  4. Run the App: Run the Python script to start the Dash web application:

    python app.py

  5. Access the App: Open a web browser and go to http://127.0.0.1:8050 (or the url mentioned in your terminal) to access the K-Means Clustering Simulator App.

Usage

  1. Select Dataset Shape: Choose the shape of the dataset from the dropdown menu (e.g., blobs, moons, circles).
  2. Adjust Parameters: Set the dataset size, number of clusters, centroid initialization method, and maximum iterations using the sliders and dropdown menus.
  3. Generate Dataset and Centroids: Click on the "Generate Data" and "Generate Centroids" buttons to create the dataset and initialize centroids.
  4. Run Simulation: Start the simulation by clicking on the "Play" button to observe the step-by-step execution of the K-Means algorithm.
  5. Visualize Results: Watch the animation of the algorithm, explore the inertia plot, and analyze the silhouette coefficient to understand the clustering process.
  6. Interact with Animation: Use the play, pause, next step, and previous step buttons to control the animation and navigate through iterations.

Dependencies

  • Dash: For building interactive web applications.
  • Plotly: For creating interactive and customizable plots.
  • NumPy: For numerical computations and array manipulations.
  • Scikit-learn: For implementing the K-Means algorithm and evaluating clustering metrics.
  • Matplotlib: For plotting graphs and visualizations.
  • OpenCV: For image processing tasks (optional).

Authors