我为我的数据集生成了一个树状图,但我不满意如何在某些级别对拆分进行排序。因此,我正在寻找一种方法来交换单个拆分的两个分支(或叶子)。
[如果我们查看底部的代码和树状图,则有两个标签11
和25
与大集群的其余部分分开。我对此真的感到不满意,希望11
和25
的分支成为拆分的右分支,而集群的其余部分成为左分支。显示的距离仍然是相同的,因此仅出于美观目的,不会更改数据。
可以这样做吗?如何?我专用于手动干预,因为在这种情况下,最佳叶子排序算法supposedly不起作用。
import numpy as np
# random data set with two clusters
np.random.seed(65) # for repeatability of this tutorial
a = np.random.multivariate_normal([10, 0], [[3, 1], [1, 4]], size=[10,])
b = np.random.multivariate_normal([0, 20], [[3, 1], [1, 4]], size=[20,])
X = np.concatenate((a, b),)
# create linkage and plot dendrogram
from scipy.cluster.hierarchy import dendrogram, linkage
Z = linkage(X, 'ward')
plt.figure(figsize=(15, 5))
plt.title('Hierarchical Clustering Dendrogram')
plt.xlabel('sample index')
plt.ylabel('distance')
dendrogram(
Z,
leaf_rotation=90., # rotates the x axis labels
leaf_font_size=12., # font size for the x axis labels
)
plt.show()
import numpy as np
import matplotlib.pyplot as plt
# random data set with two clusters
np.random.seed(65) # for repeatability of this tutorial
a = np.random.multivariate_normal([10, 0], [[3, 1], [1, 4]], size=[10,])
b = np.random.multivariate_normal([0, 20], [[3, 1], [1, 4]], size=[20,])
X = np.concatenate((a, b),)
# create linkage and plot dendrogram
from scipy.cluster.hierarchy import dendrogram, linkage
Z = linkage(X, 'ward', optimal_ordering = True)
plt.figure(figsize=(15, 5))
plt.title('Hierarchical Clustering Dendrogram')
plt.xlabel('sample index')
plt.ylabel('distance')
dendrogram(
Z,
leaf_rotation=90., # rotates the x axis labels
leaf_font_size=12., # font size for the x axis labels
distance_sort=False,
show_leaf_counts=True,
count_sort=False
)
plt.show()