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.

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().

Exercise

Visualizing and interpreting results of kmeans()

Instructions