How do you implement KMeans clustering with MiniBatchKMeans for large datasets in Scikit-learn

Question

Can i know How do you implement KMeans clustering with MiniBatchKMeans for large datasets in Scikit-learn?

Answer 1

You can implement KMeans clustering for large datasets using MiniBatchKMeans in Scikit-learn for faster and more memory-efficient performance.

Here is the code snippet you can refer to:

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

• MiniBatchKMeans(n_clusters=5, batch_size=100) optimizes clustering for large datasets by processing data in small batches.
• fit(X) trains the model on the data.
• cluster_centers_ provides the final cluster locations.
• labels_ assigns each data point to a cluster.
• Visualizes clusters and centers for better interpretability.

Hence, MiniBatchKMeans is a scalable and efficient solution for clustering large datasets, balancing speed and accuracy by using mini-batches.