k-means-clustering-algorithm

This repository contains a manually implemented version of the k-means clustering algorithm in Python. It is intended as a learning project to understand the fundamentals of clustering algorithms and unsupervised machine learning.

Overview

k-means clustering is an unsupervised learning algorithm partitioning a dataset into k distinct, non-overlapping subsets (clusters). The algorithm aims to minimize the variance within each cluster, making the clusters as distinct as possible.

Features

• Manual implementation of k-means clustering
• Euclidean distance calculation
• Centroid computation
• Grouping data points by the closest centroid
• Iterative refinement of centroids

Usage

To use the k-means clustering algorithm, follow these steps:

1. Clone the repository:

   git clone https://github.com/yourusername/k-means-clustering-algorithm.git

2. Navigate to the project directory:

   cd k-means-clustering-algorithm

3. Run the k-means algorithm with your data:

   from kmeans import k_means
   
   data = [
       (1, 2), (2, 3), (3, 4), (8, 9), (9, 10), (10, 11)
   ]
   k = 2
   centroids = k_means(data, k)
   
   print("Computed centroids:", centroids)

Functions

• transpose(data): Swaps rows and columns in a 2-D array of data.
• mean(data): Calculates the arithmetic mean of a list of numbers.
• dist(p, q): Computes the Euclidean distance between two multi-dimensional points.
• assign_data(centroids, data): Groups data points by the closest centroid.
• compute_centroids(groups): Computes the centroid of each group.
• k_means(data, k=3, iterations=50): Performs k-means clustering on the given data.