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Hierarchical clustering of the grain data

In the video, you learned that the SciPy linkage() function performs hierarchical clustering on an array of samples. Use the linkage() function to obtain a hierarchical clustering of the grain samples, and use dendrogram() to visualize the result. A sample of the grain measurements is provided in the array samples, while the variety of each grain sample is given by the list varieties.

This exercise is part of the course

Unsupervised Learning in Python

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Exercise instructions

  • Import:
    • linkage and dendrogram from scipy.cluster.hierarchy.
    • matplotlib.pyplot as plt.
  • Perform hierarchical clustering on samples using the linkage() function with the method='complete' keyword argument. Assign the result to mergings.
  • Plot a dendrogram using the dendrogram() function on mergings. Specify the keyword arguments labels=varieties, leaf_rotation=90, and leaf_font_size=6.

Hands-on interactive exercise

Have a go at this exercise by completing this sample code.

# Perform the necessary imports
from ____ import ____, ____
import ____ as ____

# Calculate the linkage: mergings
mergings = ____

# Plot the dendrogram, using varieties as labels
dendrogram(____,
           labels=____,
           leaf_rotation=____,
           leaf_font_size=____,
)
plt.show()
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