👀 Views: 3 💬 Answers: 1 📅 Created: 2025-06-11

k-means scikit-learn machine-learning Python

I'm testing a new approach and I'm relatively new to this, so bear with me. I'm implementing the K-Means clustering algorithm using Python's `scikit-learn` library (version 1.0.2) and working with an scenario where some clusters remain empty after several iterations. My dataset has around 10,000 samples with 5 features and I set the number of clusters to 3. When I print the labels after fitting the model, I sometimes see that one of the clusters has no data points assigned to it. I tried increasing the number of initializations with `n_init=10`, but it did not resolve the scenario. Here’s a simplified version of my code: ```python import numpy as np from sklearn.cluster import KMeans # Generate random data for testing np.random.seed(42) data = np.random.rand(10000, 5) # Create K-Means model kmeans = KMeans(n_clusters=3, n_init=10, random_state=42) # Fit the model kmeans.fit(data) # Check cluster labels and counts unique, counts = np.unique(kmeans.labels_, return_counts=True) label_counts = dict(zip(unique, counts)) print(label_counts) ``` When I run this code, I sometimes see output like `{0: 3333, 1: 3334, 2: 0}` indicating that one of the clusters has zero points. I also tried using a different random state, but the scenario continues. Is there a reason why K-Means might produce empty clusters, and what can I do to prevent this from happening? Any insights into this behavior would be greatly appreciated! I'm working on a web app that needs to handle this. My development environment is Windows. I'd really appreciate any guidance on this. The stack includes Python and several other technologies. The stack includes Python and several other technologies.