K Means Clustering

 

What is K-means Clustering?

K-means is a centroid-based algorithm, or a distance-based algorithm, where we calculate the distances to assign a point to a cluster. In K-Means, each cluster is associated with a centroid.

Applications of Clustering

Clustering has a large no. of applications spread across various domains. Some of the most popular applications of clustering are:

• Recommendation engines
• Market segmentation
• Social network analysis
• Search result grouping
• Medical imaging
• Image segmentation
• Anomaly detection

K-Means using Numpy:

import io

import pandas as pd

import matplotlib.pyplot as plt

from scipy.spatial.distance import cdist

import numpy as np

df2 = pd.read_csv(io.BytesIO(uploaded['dataset_3.csv']))

data = df2.values

print(data) 

def normalize(data):

     return (data - data.min(axis=0))/(data.max(axis=0)-data.min(axis=0))

data = normalize(data)

plt.plot(data[:,0],data[:,1],'.')

means = np.random.random((3,2))

plt.plot(data[:,0],data[:,1],'.')

plt.plot(means[0,0],means[0,1],'ro')

plt.plot(means[1,0],means[1,1],'go')

plt.show()

for i in range(5):

        labels = cdist(data,means).argmin(axis=1)

        for k in range (3):

            dataset = data[labels == k]

            plt.plot(dataset[:,0],dataset[:,1],'.')

            means[k] = dataset.mean(axis=0)

            plt.plot(means[k][0],means[k][1],'X')

        plt.show()

K-Means using Scikit Learn:

import numpy as np

import pandas as pd

from matplotlib import pyplot as plt

from sklearn.datasets.samples_generator import make_blobs

from sklearn.cluster import KMeans

X, y = make_blobs(n_samples=300, centers=4, cluster_std=0.60, random_state=0)

plt.scatter(X[:,0], X[:,1])

wcss = []

for i in range(1, 11):

    kmeans = KMeans(n_clusters=i, init='k-means++', max_iter=300, n_init=10, random_state=0)

    kmeans.fit(X)

    wcss.append(kmeans.inertia_)

plt.plot(range(1, 11), wcss)

plt.title('Elbow Method')

plt.xlabel('Number of clusters')

plt.ylabel('WCSS')

plt.show()

kmeans = KMeans(n_clusters=4, init='k-means++', max_iter=300, n_init=10, random_state=0)

pred_y = kmeans.fit_predict(X)

plt.scatter(X[:,0], X[:,1])

plt.scatter(kmeans.cluster_centers_[:, 0], kmeans.cluster_centers_[:, 1], s=300, c='red')

plt.show()