使用肘部法确定k-means均值的k值
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import numpy as np from sklearn.cluster import KMeans from scipy.spatial.distance import cdist import matplotlib.pyplot as plt c1x = np.random.uniform(0.5, 1.5, (1, 10)) c1y = np.random.uniform(0.5, 1.5, (1, 10)) c2x = np.random.uniform(3.5, 4.5, (1, 10)) c2y = np.random.uniform(3.5, 4.5, (1, 10)) x = np.hstack((c1x, c2x)) y = np.hstack((c1y, c2y)) X = np.vstack((x, y)).T K = range(1, 10) meanDispersions = [] for k in K: kmeans = KMeans(n_clusters=k) kmeans.fit(X) #理解为计算某个与其所属类聚中心的欧式距离 #最终是计算所有点与对应中心的距离的平方和的均值 meanDispersions.append(sum(np.min(cdist(X, kmeans.cluster_centers_, ‘euclidean‘), axis=1)) / X.shape[0]) plt.plot(K, meanDispersions, ‘bx-‘) plt.xlabel(‘k‘) plt.ylabel(‘Average Dispersion‘) plt.title(‘Selecting k with the Elbow Method‘) plt.show()
X为:
[[0.84223858 1.18059879] [0.84834276 0.84499409] [1.13263229 1.34316399] [0.95487981 0.59743761] [0.81646041 1.32361288] [0.90405171 0.54047701] [1.2723004 1.3461647 ] [0.52939142 1.03325549] [0.84592514 0.74344317] [1.07882783 1.4286598 ] [3.71702311 3.97510452] [3.95476036 3.83842502] [4.4297804 3.91854623] [4.08686159 4.15798624] [3.90406684 3.84413461] [4.32395689 4.06825926] [4.23112269 3.78578326] [3.70602931 4.08608482] [3.58690191 4.37072349] [4.38564657 4.02168693]]
随着K的增加,纵轴呈下降趋势且最终趋于稳定,那么拐点肘部处的位置所对应的k 值,不妨认为是相对最佳的类聚数量值。
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