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.