👀 Views: 237 💬 Answers: 1 📅 Created: 2025-06-08

k-means scikit-learn clustering Python

I'm integrating two systems and I need some guidance on I'm integrating two systems and I'm implementing the K-Means clustering algorithm using Python's `scikit-learn` library (version 0.24.2), and I consistently run into issues where the centroids do not stabilize after several iterations. I am using the default parameters but have tried adjusting the `max_iter` to 500 and `n_init` to 20, yet the centroids keep shifting significantly between runs, and I get a warning: `ConvergenceWarning: Number of distinct clusters (1) found smaller than n_clusters (3).` Here's the code snippet I'm currently using for fitting the model: ```python import numpy as np from sklearn.cluster import KMeans # Sample data with 10 points in 2D X = np.array([[1, 2], [1, 4], [1, 0], [4, 2], [4, 4], [4, 0], [1, 3], [4, 1], [2, 2], [3, 3]]) # K-Means implementation kmeans = KMeans(n_clusters=3, max_iter=500, n_init=20, random_state=42) kmeans.fit(X) print('Centroids:', kmeans.cluster_centers_) print('Labels:', kmeans.labels_) ``` I am especially concerned about why the algorithm is failing to find stable centroids and how it leads to fewer distinct clusters than specified. Is there a specific preprocessing step I might be missing, or should I adjust the initialization method? I would appreciate any insights or strategies to ensure stable convergence in this scenario. This issue appeared after updating to Python LTS. Hoping someone can shed some light on this. My team is using Python for this service. Am I approaching this the right way? My development environment is CentOS. What's the best practice here? Is there a better approach?