How do you implement K-means initialization in KMeans clustering using Scikit-learn

Question

Can you tell me How do you implement K-means++ initialization in KMeans clustering using Scikit-learn?

Answer 1

K-means initialization in Scikit-learn is set using the init parameter, where "k-means++" optimizes cluster center selection for faster convergence and better performance.

Here is the code snippet you can refer to:

In the above code we are using the following key points:

• Uses make_blobs to generate synthetic clusterable data.
• Applies KMeans with "k-means++" for efficient centroid initialization.
• Visualizes the clustered data and cluster centers.

Hence, using "k-means++" initialization in KMeans ensures faster convergence and more accurate clustering by starting with well-placed initial centroids.