Let's see just how easy it is to do k-means clustering using scikit-learn and Python.
The first thing we're going to do is create some random data that we want to try to cluster. Just to make it easier, we'll actually build some clusters into our fake test data. So let's pretend there's some real fundamental relationship between these data, and there are some real natural clusters that exist in it.
So to do that, we can work with this little createClusteredData() function in Python:
from numpy import random, array
#Create fake income/age clusters for N people in k clusters
def createClusteredData(N, k):
random.seed(10)
pointsPerCluster = float(N)/k
X = []
for i in range (k):
incomeCentroid = random.uniform(20000.0, 200000.0)
ageCentroid = random.uniform(20.0, 70.0)
...