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.

Diese Übung ist Teil des Kurses

Unsupervised Learning in R

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Anleitung zur Übung

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.

Interaktive Übung

Versuche dich an dieser Übung, indem du diesen Beispielcode vervollständigst.

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


# Compare methods
table(___, ___)

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