Python: Real World Machine Learning

Clustering data using the k-means algorithm

The k-means algorithm is one of the most popular clustering algorithms. This algorithm is used to divide the input data into k subgroups using various attributes of the data. Grouping is achieved using an optimization technique where we try to minimize the sum of squares of distances between the datapoints and the corresponding centroid of the cluster. If you need a quick refresher, you can learn more about k-means at http://www.onmyphd.com/?p=k-means.clustering&ckattempt=1.

How to do it…

1. The full code for this recipe is given in the kmeans.py file already provided to you. Let's look at how it's built. Create a new Python file, and import the following packages:

   import numpy as np import matplotlib.pyplot ...