Exercise

K-means: Average Silhouette Widths

So hierarchical clustering resulting in 3 clusters and the elbow method suggests 2. In this exercise use average silhouette widths to explore what the "best" value of k should be.

Instructions

100 XP

Use map_dbl() to run pam() using the oes data for k values ranging from 2 to 10 and extract the average silhouette width value from each model: model$silinfo$avg.width. Store the resulting vector as sil_width.
Build a new data frame sil_df containing the values of k and the vector of average silhouette widths.
Use the values in sil_df to plot a line plot showing the relationship between k and average silhouette width.