Skip to content
Related Articles

Related Articles

Improve Article

Analysis of test data using K-Means Clustering in Python

  • Difficulty Level : Medium
  • Last Updated : 09 Feb, 2018

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.

# 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')

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.


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.

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(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')


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

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

Attention reader! Don’t stop learning now. Get hold of all the important Machine Learning Concepts with the Machine Learning Foundation Course at a student-friendly price and become industry ready.

My Personal Notes arrow_drop_up
Recommended Articles
Page :