kmeans2.py [1 KB]
kmeans2.png [21 KB]
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 | import matplotlib.pyplot as plt import numpy as np from sklearn.cluster import KMeans X = np.array([ [6,3], [11,15], [17,12], [24,10], [20,25], [22,30], [85,70], [71,81], [60,79], [56,52], [81,91], [80,81]]) plt.scatter(X[:,0],X[:,1]) n_clusters = range(1, 10) kmeans = [KMeans(n_clusters=i) for i in n_clusters] # 모든 샘플에 대하여 제곱 오차를 계산하여 리스트에 추가한다. score = [kmeans[i].fit(X).inertia_ for i in range(len(kmeans))] plt.plot(n_clusters, score) plt.xlabel('Number of Clusters') plt.ylabel('Score') plt.title('Elbow Curve') plt.show() | cs |
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인공지능 실습