Using MiniBatch k-means to handle more data

K-means is a nice method to use; however, it is not ideal for a lot of data. This is due to the complexity of k-means. This said, we can get approximate solutions with much better algorithmic complexity using MiniBatch k-means.

Getting ready

MiniBatch k-means is a faster implementation of k-means. K-means is computationally very expensive; the problem is NP-hard.

However, using MiniBatch k-means, we can speed up k-means by orders of magnitude. This is achieved by taking many subsamples that are called MiniBatches. Given the convergence properties of subsampling, a close approximation to regular k-means is achieved provided there are good initial conditions.

How to do it...

1. Let's do some very high-level profiling of MiniBatch clustering. First, we'll look at the overall speed difference, and then we'll look at the errors in the estimates:

import numpy as np
from sklearn.datasets import make_blobs
blobs, labels = make_blobs(int(1e6), 3)

from sklearn.cluster...