Comparing kmeans() and hclust()

Comparing k-means and hierarchical clustering, you'll see the two methods produce different cluster memberships. This is because the two algorithms make different assumptions about how the data is generated. In a more advanced course, we could choose to use one model over another based on the quality of the models' assumptions, but for now, it's enough to observe that they are different.

This exercise will have you compare results from the two models on the pokemon dataset to see how they differ.

이 연습은 강의의 일부입니다

Unsupervised Learning in R

강의 보기

연습 안내

The results from running k-means clustering on the pokemon data (for 3 clusters) are stored as km.pokemon. The hierarchical clustering model you created in the previous exercise is still available as hclust.pokemon.

Using cutree() on hclust.pokemon, assign cluster membership to each observation. Assume three clusters and assign the result to a vector called cut.pokemon.
Using table(), compare cluster membership between the two clustering methods. Recall that the different components of k-means model objects can be accessed with the $ operator.

실습형 인터랙티브 연습

이 예제를 이 샘플 코드를 완성하여 풀어보세요.

# Apply cutree() to hclust.pokemon: cut.pokemon


# Compare methods
table(___, ___)

코드 편집 및 실행