The KMeans clustering algorithm can be used to cluster observed data automatically. All of its centroids are stored in the attribute cluster_centers.
In this article we’ll show you how to plot the centroids.
Related course:
Data Science and Machine Learning with Python – Hands On!
KMeans cluster centroids
We want to plot the cluster centroids like this:
First thing we’ll do is to convert the attribute to a numpy array:
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centers = np.array(kmeans_model.cluster_centers_) |
This array is one dimensional, thus we plot it using:
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plt.scatter(centers[:,0], centers[:,1], marker="x", color='r') |
We can plot the cluster centroids using the code below.
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# clustering dataset from sklearn.cluster import KMeans from sklearn import metrics import numpy as np import matplotlib.pyplot as plt x1 = np.array([3, 1, 1, 2, 1, 6, 6, 6, 5, 6, 7, 8, 9, 8, 9, 9, 8]) x2 = np.array([5, 4, 6, 6, 5, 8, 6, 7, 6, 7, 1, 2, 1, 2, 3, 2, 3]) # create new plot and data plt.plot() X = np.array(list(zip(x1, x2))).reshape(len(x1), 2) colors = ['b', 'g', 'c'] markers = ['o', 'v', 's'] # KMeans algorithm K = 3 kmeans_model = KMeans(n_clusters=K).fit(X) print(kmeans_model.cluster_centers_) centers = np.array(kmeans_model.cluster_centers_) plt.plot() plt.title('k means centroids') for i, l in enumerate(kmeans_model.labels_): plt.plot(x1[i], x2[i], color=colors[l], marker=markers[l],ls='None') plt.xlim([0, 10]) plt.ylim([0, 10]) plt.scatter(centers[:,0], centers[:,1], marker="x", color='r') plt.show() |