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 as plt
   from sklearn import metrics
   from sklearn.cluster import KMeans
   
   import utilities

2. Let's load the input data and define the number of clusters. We will use the data_multivar.txt file that's already provided to you:

   data = utilities.load_data...