This repository contains an implementation of the K-Means clustering algorithm in Python, which is a popular unsupervised machine learning technique used for partitioning data into groups or clusters. This implementation provides a class, K_mean, that allows you to perform K-Means clustering on a given dataset.

To use this K-Means clustering implementation, follow these steps:

1. Clone the Repository:

   git clone https://github.com/actuallyroy/K-Mean.git
   cd K-Mean

2. Import the K_mean Class:

   Import the K_mean class in your Python script:

   from K_Mean import K_mean

3. Prepare Your Data:

   Create or load your dataset and make sure it is in the appropriate format. You can use the provided example data or prepare your own data.

   Example data format:

   x = [14, 48, 83, ...]  # List of data points for the first dimension
   y = [64, 59, 10, ...]  # List of data points for the second dimension
   z = [62, 13, 2, ...]   # List of data points for the third dimension
   data = list(zip(x, y, z, x))  # Combine the dimensions into a list of tuples

4. Create a K-Mean Object:

   Create a K_mean object by providing your data and the number of clusters (k) you want to create. Optionally, you can provide initial centroids:

   obj = K_mean(data, k=4, centroids=[])  # Initialize with your data, k value, and optional initial centroids

5. Perform Clustering:

   You can perform clustering by calling the getClusters method:

   obj.getClusters()  # Perform K-Means clustering

6. Visualize the Clusters:

   You can visualize the clustered data using the plot method, which provides a 2D or 3D scatter plot of the clusters:

   obj.plot()  # Visualize the clustered data

7. Customization and Analysis:

   Feel free to customize the K-Means implementation or analyze the results, such as calculating cluster centroids, cluster inertia, or other metrics.

An example of using the K-Means clustering implementation is provided in the script example.py. You can run this script to see how the K-Means algorithm works with sample data.

This implementation relies on the following Python libraries:

• random: For generating random numbers.
• matplotlib: For data visualization.
• math: For mathematical calculations.

• actuallyroy