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') plt.hist(Z,256,[0,256]) plt.show() |
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.
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() |
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.
