凝聚聚类层次可视化

Question

如何将凝聚聚类形成的层次结构可视化为树状图。我有一个大小为(400,400)的预计算距离矩阵。

clusterer= AgglomerativeClustering(n_clusters=32,affinity="precomputed",linkage="average").fit(distance_matrix)

我如何清晰地将形成这32个集群的结果可视化为树状图？我试着可视化集群，但由于它们是32，颜色不能清楚地区分它们。

colors_clusters = clusterer.labels_
fig = plt.figure(figsize=(10,7))
plt.xlabel('median_score', family='Arial', fontsize=9)
plt.ylabel('count_intersections', family='Arial', fontsize=9)
plt.title('Heliopolis', family='Arial', fontsize=12)
plt.scatter(clusters_df['median_score'], clusters_df['count_intersections'], c=colors_clusters, edgecolors='black', s=50)
plt.show()

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Answer 1

层次聚类的一种著名的可视化方法是使用dendrogram。您可以在sklearn library中找到一个绘图示例。您也可以在scipy library中找到示例。

您可以在此处找到前一个链接中的示例：

import numpy as np

from matplotlib import pyplot as plt
from scipy.cluster.hierarchy import dendrogram
from sklearn.datasets import load_iris
from sklearn.cluster import AgglomerativeClustering


def plot_dendrogram(model, **kwargs):
    # Create linkage matrix and then plot the dendrogram

    # create the counts of samples under each node
    counts = np.zeros(model.children_.shape[0])
    n_samples = len(model.labels_)
    for i, merge in enumerate(model.children_):
        current_count = 0
        for child_idx in merge:
            if child_idx < n_samples:
                current_count += 1  # leaf node
            else:
                current_count += counts[child_idx - n_samples]
        counts[i] = current_count

    linkage_matrix = np.column_stack([model.children_, model.distances_,
                                      counts]).astype(float)

    # Plot the corresponding dendrogram
    dendrogram(linkage_matrix, **kwargs)


iris = load_iris()
X = iris.data

# setting distance_threshold=0 ensures we compute the full tree.
model = AgglomerativeClustering(distance_threshold=0, n_clusters=None)

model = model.fit(X)
plt.title('Hierarchical Clustering Dendrogram')
# plot the top three levels of the dendrogram
plot_dendrogram(model, truncate_mode='level', p=3)
plt.xlabel("Number of points in node (or index of point if no parenthesis).")
plt.show()