How can you visualize K-means clusters and their centers using a scatter plot in R?

Conditions

• Requires at least two numeric variables for plotting.
• Requires cluster assignments and center coordinates from the kmeans output.

Reasoning, step by step

1. Select two variables to plot on the x and y axes.
2. Use the plot() function to create a scatter plot.
3. Set the col argument in plot() to the cluster assignments from the kmeans result.
4. Use the points() function to add the cluster centers to the plot.
5. Customize the appearance of the center points using arguments like pch and cex.

Example

The video shows the code: plot(iris_data[c("Sepal.Length", "Sepal.Width")], col=kmeans_iris_result$cluster) followed by points(kmeans_iris_result$centers[,c("Sepal.Length", "Sepal.Width")], col=1:3, pch=23, cex=3).

Common misconceptions

• Forgetting to color the points by cluster assignment.
• Not adding the cluster centers to the plot, making it harder to see the partitioning.

Watch the explanation

Explore next

• What information is contained in the object returned by the R kmeans function?
• How do you run K-means clustering in R and specify the number of clusters?
• What is the definition of K-means clustering?