Hierarchical clustering of case data
The goal of this exercise is to do hierarchical clustering of the observations. Recall from Chapter 2 that this type of clustering does not assume in advance the number of natural groups that exist in the data.
As part of the preparation for hierarchical clustering, distance between all pairs of observations are computed. Furthermore, there are different ways to link clusters together, with single, complete, and average being the most common linkage methods.
This exercise is part of the course
Unsupervised Learning in R
Exercise instructions
The variables you created before, wisc.data, diagnosis, wisc.pr, and pve, are available in your workspace.
- Scale the
wisc.datadata and assign the result todata.scaled. - Calculate the (Euclidean) distances between all pairs of observations in the new scaled dataset and assign the result to
data.dist. - Create a hierarchical clustering model using complete linkage. Manually specify the
methodargument tohclust()and assign the results towisc.hclust.
Hands-on interactive exercise
Have a go at this exercise by completing this sample code.
# Scale the wisc.data data: data.scaled
# Calculate the (Euclidean) distances: data.dist
# Create a hierarchical clustering model: wisc.hclust