C-KMEANS

C-KMEANS is a C implementation of the K-means clustering algorithm. This project aims to provide an efficient and flexible tool for clustering datasets, focusing on speed and performance. It allows users to regenerate datasets and experiment with the K-means algorithm for various applications in data mining and machine learning.

step shows how the kmeans algorithm partitions the dataset

Table of Contents

• Overview
• Features
• Installation
• Usage
  • Running the Program
  • Dataset Generation
  • K-means Clustering
• Project Structure
• Contributing
• License

Overview

K-means clustering is a widely used algorithm in machine learning and data mining that partitions a dataset into clusters based on feature similarity. This project provides a simple yet efficient implementation in C, designed for performance with minimal dependencies.

Features

• Regenerate Datasets: Easy dataset generation for experimentation.
• Efficient Clustering: Optimized for speed, capable of handling large datasets.
• Modular Codebase: Well-structured for ease of extension or modification.

Installation

To install and use this project, follow these steps:

1. Clone the repository:

   git clone https://github.com/n0connect/C-KMEANS.git

2. Navigate to the project directory::

   cd C-KMEANS

3. Navigate to the project directory::

   ./build.sh
   

**Note: Make sure you have a C compiler (e.g., gcc) installed and set up on your system

Usage

Running the program

KEY_R = Regenerate the dataset KEY_SPACE = One itteration for the set

Experimental

**It was just an experimental test project.