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.
이 연습은 강의의 일부입니다
Unsupervised Learning in R
연습 안내
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.
실습형 인터랙티브 연습
이 예제를 이 샘플 코드를 완성하여 풀어보세요.
# Scale the wisc.data data: data.scaled
# Calculate the (Euclidean) distances: data.dist
# Create a hierarchical clustering model: wisc.hclust