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
Exercise instructions
- Import:
linkage
anddendrogram
fromscipy.cluster.hierarchy
.matplotlib.pyplot
asplt
.
- Perform hierarchical clustering on
samples
using thelinkage()
function with themethod='complete'
keyword argument. Assign the result tomergings
. - Plot a dendrogram using the
dendrogram()
function onmergings
. Specify the keyword argumentslabels=varieties
,leaf_rotation=90
, andleaf_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()