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.

この演習はコースの一部です

Unsupervised Learning in R

コースを見る

演習の手順

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.

実践的なインタラクティブ演習

このサンプルコードを完成させて、この演習に挑戦してみましょう。

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


# Compare methods
table(___, ___)

コードを編集して実行