kMeans-Clustering

A Python implementation of the K-Means clustering algorithm with silhouette analysis to evaluate cluster quality.

This project implements the K-Means clustering algorithm in Python, providing a method for clustering datasets and evaluating the quality of clustering using silhouette analysis. The implementation allows you to run K-Means on your own dataset and plot the silhouette coefficient for various numbers of clusters to help choose the best k-value.

Features

• Load and preprocess datasets from a CSV file.
• Perform K-Means clustering with customisable number of clusters and iterations.
• Evaluate cluster quality using the silhouette coefficient.
• Visualise the silhouette scores for different cluster sizes.

Prerequisites

The following Python libraries are required to run the code:

• numpy
• pandas
• matplotlib

Dataset

The code expects a dataset in CSV format with columns separated by spaces. The first column is ignored (e.g., for non-numeric labels). The dataset is not included in this repository due to licensing concerns, but you can provide your own dataset.

• The dataset should contain numeric columns representing features.
• The correct file path should be provided when running the script.

Example Output

After running the code, you will see a silhouette plot like this: