K-Means Clustering Algorithm

For a university seminar "Introduction to Machine Learning", I've developed a simple Python program to implement and demonstrate the K-Means clustering algorithm.

Overview

The program leverages basic Python libraries such as random, math, and matplotlib for executing and visualizing the K-Means algorithm.

Functionality

• assign_clusters(data, centers):

  • Takes data points and cluster centers as input.
  • Assigns each data point to the nearest cluster center.
  • Returns a list of clusters, indicating the assignment of each data point to a cluster.

• update_centers(data, clusters, k):

  • Based on the assignment of data points to clusters, this function recalculates the centers for each cluster.
  • Returns the updated cluster centers.

• k_means(data, k, initial_centers):

  • Implements the core K-Means algorithm.
  • Continuously assigns data points to clusters and recalculates cluster centers until convergence.

• main():

  • Demonstrates the K-Means algorithm on a sample dataset of 2-dimensional vectors.
  • Plots the dataset and the resulting clusters using matplotlib.

To execute the demonstration, simply run the script. The visualization will display the data points colored by their assigned cluster and the cluster centers marked with a red 'x'.