Step 5: Testing the algorithm
Here is the code for getting the labels property of the K-means clustering example dataset; that is, how the data points are categorized into the two clusters.
Kmean.labels_
Here is the result of running the above K-means algorithm code:
array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1])
As you can see above, 50 data points belong to the 0 cluster while the rest belong to the 1 cluster.
For example, let’s use the code below for predicting the cluster of a data point:
sample_test=np.array([-3.0,-3.0])second_test=sample_test.reshape(1, -1)Kmean.predict(second_test)
Here is the result:
array([0])
It shows that the test data point belongs to the 0 (green centroid) cluster.
Here is the entire K-means clustering algorithm code in Python:
import pandas as pdimport numpy as npimport matplotlib.pyplot as pltfrom sklearn.cluster import KMeans%matplotlib inlineX= -2 * np.random.rand(100,2)X1 = 1 + 2 * np.random.rand(50,2)X[50:100, :] = X1plt.scatter(X[ : , 0], X[ :, 1], s = 50, c = ‘b’)plt.show()from sklearn.cluster import KMeansKmean = KMeans(n_clusters=2)Kmean.fit(X)Kmean.cluster_centers_plt.scatter(X[ : , 0], X[ : , 1], s =50, c=’b’)plt.scatter(-0.94665068, -0.97138368, s=200, c=’g’, marker=’s’)plt.scatter(2.01559419, 2.02597093, s=200, c=’r’, marker=’s’)plt.show()Kmean.labels_sample_test=np.array([-3.0,-3.0])second_test=sample_test.reshape(1, -1)Kmean.predict(second_test)
K-means clustering is an extensively used technique for data cluster analysis.
It is easy to understand, especially if you accelerate your learning using a K-means clustering tutorial. Furthermore, it delivers training results quickly.
However, its performance is usually not as competitive as those of the other sophisticated clustering techniques because slight variations in the data could lead to high variance.
Furthermore, clusters are assumed to be spherical and evenly sized, something which may reduce the accuracy of the K-means clustering Python results.
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