19 lines
628 B
Python
19 lines
628 B
Python
import numpy as np
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import matplotlib.pyplot as plt
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from sklearn.cluster import KMeans
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from sklearn.datasets import make_blobs
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# Erzeuge 300 Datenpunkten und 4 Clustern
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data, _ = make_blobs(n_samples=300, centers=4, cluster_std=0.60, random_state=0)
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# Wende K-Means Clustering an
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kmeans = KMeans(n_clusters=4)
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kmeans.fit(data)
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predicted_labels = kmeans.predict(data)
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# Zeige die Cluster und Zentroide
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plt.scatter(data[:, 0], data[:, 1], c=predicted_labels, s=50, cmap='viridis')
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centers = kmeans.cluster_centers_
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plt.scatter(centers[:, 0], centers[:, 1], c='red', s=200, alpha=0.75, marker='X')
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plt.title("k-Means")
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plt.show() |