Unexpected Results from K-Means Clustering in Python - Centroid Initialization Issues
I'm working on a project and hit a roadblock. I'm wondering if anyone has experience with I'm currently implementing the K-Means clustering algorithm using Python's `scikit-learn` library, and I'm working with issues with the clustering results. When I try to run the algorithm with different initial centroid configurations, I'm getting inconsistent cluster assignments that seem unrelated to the input data. Here's a snippet of my implementation: ```python from sklearn.datasets import make_blobs from sklearn.cluster import KMeans import matplotlib.pyplot as plt # Create synthetic data X, y_true = make_blobs(n_samples=300, centers=4, cluster_std=0.60, random_state=0) # Attempt to cluster with 4 clusters kmeans = KMeans(n_clusters=4, init='random', n_init=10, max_iter=300, random_state=42) # Fit the model kmeans.fit(X) # Get the labels and centroids labels = kmeans.labels_ centroids = kmeans.cluster_centers_ # Plotting results plt.scatter(X[:, 0], X[:, 1], c=labels, s=30, cmap='viridis') plt.scatter(centroids[:, 0], centroids[:, 1], c='red', s=200, alpha=0.75) plt.title('K-Means Clustering') plt.show() ``` The question arises when I initialize the centroids randomly. Instead of finding distinct clusters, the algorithm sometimes produces results where the centroids are very close to each other, leading to overlapping clusters. On different runs, the clustering results vary significantly, which is unexpected since I'm using the same random seed. I've tried using different initialization methods like `init='k-means++'` but noticed that the results are still not stable. I also checked the `n_init` parameter; increasing it to 20 provided slightly better results, but the inconsistency remains. Is there a best practice for initializing centroids in K-Means clustering to avoid these issues? Are there specific configurations or additional techniques I should consider for better stability in my results? Any suggestions would be helpful. I'd really appreciate any guidance on this.