我对python和集群的最佳数量还不熟悉。现在我的任务是分析两组数据,并使用肘部和轮廓法确定其最佳Kmean
X表示标准化之前的原始数据
我使用肘部法查看不同k值下的wcss值,使用轮廓法查看轮廓分数
from sklearn import preprocessing
from sklearn.metrics import silhouette_score
# normalize the data attributes
normalized = preprocessing.normalize(X)
#print("Normalized Data = ", normalized)
Sum_of_squared_distances = []
K = range(2,15)
for k in K:
km = KMeans(n_clusters=k)
km = km.fit(normalized)
Sum_of_squared_distances.append(km.inertia_)
plt.plot(K, Sum_of_squared_distances, 'bx-')
plt.xlabel('Number of clusters')
plt.ylabel('Sum_of_squared_distances')
plt.title('Elbow Method For Optimal k')
plt.show()
sil = []
for k in range(2, 15):
kmeans = KMeans(n_clusters = k).fit(normalized)
preds = kmeans.fit_predict(normalized)
sil.append(silhouette_score(normalized, preds, metric = 'euclidean'))
plt.plot(range(2, 15), sil, 'bx-')
plt.title('Silhouette Method For Optimal k')
plt.xlabel('Number of clusters')
plt.ylabel('Sil')
plt.show()
for i in range(len(sil)):
print(str(i+2) +":"+ str(sil[i]))
有人能建议我如何选择最佳Kmean吗?因为据我了解
plt.show()的结果是什么?难道你不应该看一看图,看看线图开始水平的k值吗?在下图中,最佳k值为5。见https://blog.cambridgespark.com/how-to-determine-the-optimal-number-of-clusters-for-k-means-clustering-14f27070048f。这也是我在大学里学习确定k值的方法
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