October 2021
Intermediate to advanced
289 pages
8h 31m
Chinese
Content preview from 数据科学中的实用统计学(第2版)
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for (i in 2:14) {
kmCluster <- kmeans(df, centers=i, nstart=50, iter.max=100)
pct_var[i-1, 'pct_var'] <- kmCluster$betweenss / totalss
}
对于
Kmeans
的结果，我们可以从
inertia_
属性中得到这些信息。在将它转换为
pandas
数
据框之后，就可以使用数据框的
plot
方法创建这个图形：
inertia = []
for n_clusters in range(2, 14):
kmeans = KMeans(n_clusters=n_clusters, random_state=0).fit(top_sp)
inertia.append(kmeans.inertia_ / n_clusters)
inertias = pd.DataFrame({'n_clusters': range(2, 14), 'inertia': inertia})
ax = inertias.plot(x='n_clusters', y='inertia')
plt.xlabel('Number of clusters(k)')
plt.ylabel('Average Within-Cluster Squared Distances')
plt.ylim((0, 1.1 * inertias.inertia.max()))
ax.legend().set_visible(False) ...
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