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Implementing K-means clustering with SciPy by splitting random data in 2 clusters?
K-means clustering algorithm, also called flat clustering, is a method of computing the clusters and cluster centers (centroids) in a set of unlabeled data. It iterates until we find the optimal centroid. The clusters, we might think of a group of data points whose inter-point distances are small as compared to the distances to the point outside of that cluster. The number of clusters identified from unlabeled data is represented by ‘K’ in K-means algorithm.
Given an initial set of K centers, the K-means clustering algorithm can be done using SciPy library by executing by the following steps −
Step1− Data point normalization
Step2− Computing the Centroids which is referred to as code. Here, the 2-dimensional array of centroids is referred to as a code book.
Step3− Cluster formation and assigning the data points. It is referred to as mapping from the code book.
Example
#importing the required Python libraries :
import numpy as np
from numpy import vstack,array
from numpy.random import rand
from scipy.cluster.vq import whiten, kmeans, vq
from pylab import plot,show
#Random data generation :
data = vstack((rand(200,2) + array([.5,.5]),rand(150,2)))
#Normalizing the data :
data = whiten(data)
# computing K-Means with K = 2 (2 clusters)
centroids, mean_value = kmeans(data, 2)
print("Code book :
", centroids, "
")
print("Mean of Euclidean distances :", mean_value.round(4))
# mapping the centroids
clusters, _ = vq(data, centroids)
print("Cluster index :", clusters, "
")
#Plotting using numpy's logical indexing
plot(data[clusters==0,0],data[clusters==0,1],'ob',data[clusters==1,0],data[clusters==1,1],'or')
plot(centroids[:,0],centroids[:,1],'sg',markersize=8)
show()Output
Code book : [[2.68379425 2.77892846] [1.34079677 1.27029728]] Mean of Euclidean distances : 0.9384 Cluster index : [0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 1 0 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 1 0 1 0 1 1 1 1 0 1 1 1 1 0 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 0 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 0 1 1 1 1 1 1 1 1 1 0 1 1 1 0 1 0 1 1 1 1 1 0 1 0 1 1 1 0 1 0 1 1 1 1 1 1 1 1 0 1 1 1 1 1 0 1 1 0 0 0 1 1 1 1 1 1 1 1 1 1 1 0 1 1 0 0 1 1 0 0 1 0 1 1 1 1 1 1 0 1 1 1 1 1 1 0 1 1 0 1 1 1 0]
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