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Exercise

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.

Instructions

100 XP

The variables you created before, wisc.data, diagnosis, wisc.pr, and pve, are available in your workspace.

  • Scale the wisc.data data and assign the result to data.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 method argument to hclust() and assign the results to wisc.hclust.