Clustering Analysis

Overview

This analysis performs K-Means clustering on a dataset of 986 samples with 49 features (25 continuous, 24 binary). The optimal number of clusters is determined through consensus voting across multiple validation metrics.

Setup

Creating the Conda Environment

1. Create the conda environment from the provided environment file:

conda env create -f environment.yml

2. Activate the environment:

conda activate clustering_analysis

Main Analysis

python clustering_analysis.py

Performs K-Means clustering with evaluation and visualization.