K Means Clustering with Python

K Means Clustering is an unsupervised learning algorithm that tries to cluster data based on their similarity. Unsupervised learning means that there is no outcome to be predicted, and the algorithm just tries to find patterns in the data. In k means clustering, we have the specify the number of clusters we want the data to be grouped into. The algorithm randomly assigns each observation to a cluster, and finds the centroid of each cluster. Then, the algorithm iterates through two steps: Reassign data points to the cluster whose centroid is closest. Calculate new centroid of each cluster. These two steps are repeated till the within cluster variation cannot be reduced any further. The within cluster variation is calculated as the sum of the euclidean distance between the data points and their respective cluster centroids.

Import Libraries



In [1]:

    
import seaborn as sns
import matplotlib.pyplot as plt
%matplotlib inline

Create some data



In [2]:

    
from sklearn.datasets import make_blobs



In [6]:

    
#create data 
data = make_blobs(n_samples=200,n_features=2,centers=4,cluster_std=1.8,random_state=101)

Visualize data



In [7]:

    
plt.scatter(data[0][:,0],data[0][:,1],c=data[1],cmap='rainbow')









    Out[7]:





<matplotlib.collections.PathCollection at 0x7f85c38894d0>

Creating Clusters



In [8]:

    
from sklearn.cluster import KMeans



In [9]:

    
kmeans = KMeans(n_clusters=4)



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kmeans.fit(data[0])









    Out[10]:





KMeans(algorithm='auto', copy_x=True, init='k-means++', max_iter=300,
    n_clusters=4, n_init=10, n_jobs=1, precompute_distances='auto',
    random_state=None, tol=0.0001, verbose=0)



In [11]:

    
kmeans.cluster_centers_









    Out[11]:





array([[-4.13591321,  7.95389851],
       [-9.46941837, -6.56081545],
       [ 3.71749226,  7.01388735],
       [-0.0123077 ,  2.13407664]])



In [12]:

    
kmeans.labels_









    Out[12]:





array([0, 2, 3, 2, 2, 1, 2, 3, 2, 3, 0, 3, 2, 2, 0, 3, 2, 3, 1, 0, 1, 3, 3,
       1, 0, 1, 1, 3, 2, 2, 0, 1, 2, 3, 3, 0, 1, 1, 1, 3, 1, 0, 0, 0, 3, 2,
       0, 3, 1, 3, 3, 0, 2, 3, 1, 0, 3, 3, 0, 2, 1, 2, 1, 0, 2, 3, 1, 2, 2,
       1, 2, 3, 1, 3, 1, 2, 2, 3, 0, 3, 3, 1, 2, 1, 3, 3, 3, 0, 3, 1, 1, 1,
       1, 3, 3, 1, 2, 0, 1, 2, 3, 1, 3, 3, 2, 3, 1, 2, 1, 1, 2, 0, 0, 2, 1,
       2, 0, 0, 2, 0, 3, 0, 3, 0, 3, 2, 0, 3, 1, 0, 0, 0, 3, 1, 1, 0, 2, 0,
       2, 3, 1, 2, 1, 0, 0, 2, 3, 1, 0, 0, 0, 0, 3, 2, 3, 0, 2, 2, 2, 3, 2,
       3, 3, 0, 1, 0, 3, 2, 0, 3, 2, 3, 0, 2, 3, 0, 2, 2, 1, 2, 0, 1, 1, 0,
       1, 1, 1, 1, 1, 3, 1, 2, 2, 0, 1, 3, 2, 2, 1, 3], dtype=int32)



In [14]:

    
f, (ax1, ax2) = plt.subplots(1, 2, sharey=True,figsize=(10,6))
ax1.set_title('K Means')
ax1.scatter(data[0][:,0],data[0][:,1],c=kmeans.labels_,cmap='rainbow')
ax2.set_title("Original")
ax2.scatter(data[0][:,0],data[0][:,1],c=data[1],cmap='rainbow')









    Out[14]:





<matplotlib.collections.PathCollection at 0x7f85bf57f850>

The End!



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