# Plot Hierarchical Clustering Dendrogram#

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

```import numpy as np
from matplotlib import pyplot as plt
from scipy.cluster.hierarchy import dendrogram

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

[model.children_, model.distances_, counts]
).astype(float)

# Plot the corresponding dendrogram

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()
```

Total running time of the script: (0 minutes 0.121 seconds)

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