Silhouette analysis

Silhouette analysis allows you to calculate how similar each observations is with the cluster it is assigned relative to other clusters. This metric (silhouette width) ranges from -1 to 1 for each observation in your data and can be interpreted as follows:

Values close to 1 suggest that the observation is well matched to the assigned cluster
Values close to 0 suggest that the observation is borderline matched between two clusters
Values close to -1 suggest that the observations may be assigned to the wrong cluster

In this exercise you will leverage the pam() and the silhouette() functions from the cluster library to perform silhouette analysis to compare the results of models with a k of 2 and a k of 3. You'll continue working with the lineup dataset.

Pay close attention to the silhouette plot, does each observation clearly belong to its assigned cluster for k = 3?

This is a part of the course

“Cluster Analysis in R”

View Course

Exercise instructions

Generate a k-means model pam_k2 using pam() with k = 2 on the lineup data.
Plot the silhouette analysis using plot(silhouette(model)).
Repeat the first two steps for k = 3, saving the model as pam_k3.
Make sure to review the differences between the plots before proceeding (especially observation 3) for pam_k3.

Hands-on interactive exercise

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

library(cluster)

# Generate a k-means model using the pam() function with a k = 2
pam_k2 <- pam(___, k = ___)

# Plot the silhouette visual for the pam_k2 model
plot(silhouette(___))

# Generate a k-means model using the pam() function with a k = 3
pam_k3 <- ___

# Plot the silhouette visual for the pam_k3 model

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