Visualizing and interpreting results of kmeans()

One of the more intuitive ways to interpret the results of k-means models is by plotting the data as a scatter plot and using color to label the samples' cluster membership. In this exercise, you will use the standard plot() function to accomplish this.

To create a scatter plot, you can pass data with two features (i.e., columns) to plot() with an extra argument col = km.out$cluster, which sets the color of each point in the scatter plot according to its cluster membership.

This exercise is part of the course

Unsupervised Learning in R

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Exercise instructions

x and km.out are available in your workspace. Using the plot() function to create a scatter plot of data x:

Color the dots on the scatterplot by setting the col argument to the cluster component in km.out.
Title the plot "k-means with 3 clusters" using the main argument to plot().
Ensure there are no axis labels by specifying "" for both the xlab and ylab arguments to plot().

Hands-on interactive exercise

Have a go at this exercise by completing this sample code.

# Scatter plot of x

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