凝聚聚类层次可视化

问题描述

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) 的预先计算的距离矩阵。

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

如何将形成 32 个聚类的结果清晰地可视化为树状图？我尝试将这些集群可视化，但由于它们是 32 个，因此颜色并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',fontsize=9)
plt.title('Heliopolis',fontsize=12)
plt.scatter(clusters_df['median_score'],clusters_df['count_intersections'],c=colors_clusters,edgecolors='black',s=50)
plt.show()

解决方法

一种著名的分层聚类可视化方法是使用 dendrogram。您可以在 sklearn library 中找到绘图示例。您也可以在 scipy library 中找到示例。

您可以在此处从前一个链接中找到示例：

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()