凝聚聚类层次结构可视化

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)

如何将形成 32 个簇的结果清晰地可视化为树状图？我尝试可视化这些簇，但由于它们是 32 个，所以颜色无法清楚地区分它们。

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()

Answer 1

著名的层次聚类可视化方法之一也在使用 dendrogram. You can find a plot example in sklearn library. You can find examples in scipy library。

您可以在此处找到前 link 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()

凝聚聚类层次结构可视化

Agglomerative Clustering Hierarchy Visualization

python

hierarchical-clustering

scipy

scikit-learn