KMeans Clustering

By: Debasis Das (17-Feb-2021)

KMeans Clustering using SKLearn
Plotting the cluster centroid with the cluster points

import pandas as pd
import numpy as np
from sklearn.cluster import KMeans
from sklearn.decomposition import PCA
import matplotlib.pyplot as plt


A = [
[10,10],
[11,10],
[12,10],
[10,11],
[15,17],
[13.5,15],
[15,16],
[17,18],
[14.5,19],
[20,20],
[13.5,14.5]
]

df = pd.DataFrame(A)
number_clusters = 3
pca = PCA(n_components=2) 
pca.fit(df)
x_pca = pca.fit_transform(df)
km = KMeans(
    n_clusters=number_clusters, init='random',
    n_init=10, max_iter=300, 
    tol=1e-04, random_state=0
).fit(x_pca)


kmeans_labels = km.labels_
unique_labels = np.unique(kmeans_labels)
for i in unique_labels:
    plt.scatter(
        x_pca[kmeans_labels == i, 0], x_pca[kmeans_labels == i, 1]
    )
cluster_centroids = km.cluster_centers_
print("cluster_centroids = ",cluster_centroids)
print("kmeans_labels = ",kmeans_labels)
plt.scatter(cluster_centroids[:,0], cluster_centroids[:,1],linewidths = 3, s=150,marker="x", color='r')

Output:

cluster_centroids =  [[ 6.38423083 -1.0142913 ]
 [ 1.67176795  0.63576739]
 [-5.28182536 -0.28756359]]
kmeans_labels =  [2 2 2 2 1 1 1 0 1 0 1]

If you are interested to see how the KMeans clustering can be implemented in Python you can check the below link http://www.knowstack.com/notebooks/KMeans_ManualApproach.html