Analysis of test data using K-Means Clustering in Python

This article demonstrates an illustration of K-means clustering on a sample random data using open-cv library.

Pre-requisites: Numpy, OpenCV, matplot-lib
Let’s first visualize test data with Multiple Features using matplot-lib tool.

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# importing required tools
import numpy as np
from matplotlib import pyplot as plt
  
# creating two test data
X = np.random.randint(10,35,(25,2))
Y = np.random.randint(55,70,(25,2))
Z = np.vstack((X,Y))
Z = Z.reshape((50,2))
  
# convert to np.float32
Z = np.float32(Z)
  
plt.xlabel('Test Data')
plt.ylabel('Z samples')
  
plt.hist(Z,256,[0,256])
  
plt.show()

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Here ‘Z’ is an array of size 100, and values ranging from 0 to 255. Now, reshaped ‘z’ to a column vector. It will be more useful when more than one features are present. Then change the data to np.float32 type.

Output:

Now, apply the k-Means clustering algorithm to the same example as in the above test data and see its behavior.
Steps Involved:
1) First we need to set a test data.
2) Define criteria and apply kmeans().
3) Now separate the data.
4) Finally Plot the data.



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import numpy as np
import cv2
from matplotlib import pyplot as plt
  
X = np.random.randint(10,45,(25,2))
Y = np.random.randint(55,70,(25,2))
Z = np.vstack((X,Y))
  
# convert to np.float32
Z = np.float32(Z)
  
# define criteria and apply kmeans()
criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 10, 1.0)
ret,label,center = cv2.kmeans(Z,2,None,criteria,10,cv2.KMEANS_RANDOM_CENTERS)
  
# Now separate the data
A = Z[label.ravel()==0]
B = Z[label.ravel()==1]
  
# Plot the data
plt.scatter(A[:,0],A[:,1])
plt.scatter(B[:,0],B[:,1],c = 'r')
plt.scatter(center[:,0],center[:,1],s = 80,c = 'y', marker = 's')
plt.xlabel('Test Data'),plt.ylabel('Z samples')
plt.show()

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Output:

This example is meant to illustrate where k-means will produce intuitively possible clusters.

Applications:
1) Identifying Cancerous Data.
2) Prediction of Students’ Academic Performance.
3) Drug Activity Prediction.

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