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Plot Hierarchical Clustering Dendrogram#

This example plots the corresponding dendrogram of a hierarchical clustering using AgglomerativeClustering and the dendrogram method available in scipy.

Hierarchical Clustering Dendrogram

importnumpyasnp
frommatplotlibimport pyplot as plt
fromscipy.cluster.hierarchyimport dendrogram
fromsklearn.clusterimport AgglomerativeClustering
fromsklearn.datasetsimport load_iris
defplot_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 ()