K-Means Clustering

K-Means clustering is an unsupervised learning technique. This technique is used to group the data points that are showing similar characteristics and dissimilar from others

In K-Means, K represents number of clusters

Advantages

• Scales well
• Efficient

Disadvantages

• Choosing K

When to Use? 

• Normally distributed data
• Large number of samples
• Limited number of clusters

Use Cases 

• Document classification
• Customer segmentation

Python Code

from sklearn.cluster import KMeans

import numpy as np

X = np.array([[1, 2], [1, 4], [1, 0],

...               [10, 2], [10, 4], [10, 0]])

kmeans = KMeans(n_clusters=2, random_state=0).fit(X)

kmeans.labels_

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

kmeans.predict([[0, 0], [12, 3]])

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

kmeans.cluster_centers_

array([[10.,  2.],

       [ 1.,  2.]])