Related article : Cluster Analysis using Tableau and R – Part-1
We have performed clustering analysis from both Tableau and R in my previous post. Tableau uses k-means algorithm for cluster analysis which partitions the data into k clusters with a center or mean value of all the points in each. Clustering is based on the distance each measure lies from the center.
Let’s look into that in detail.
First generate a cluster scatter plot in Tableau as we did in part -1 using Iris data set.
Right click on the cluster that we added and choose Describe Clusters option
This provides you the clustering details given below
Now lets perform the k-means clustering from R and print the cluster
#copying iris to myiris variable myiris<-iris #Remove Species column myiris$Species <- NULL #clustering kmeans.result <- kmeans(myiris,3) #print the cluster data kmeans.result
Check the cluster means against that of Tableau cluster centers. Aren’t these comparable? However Tableau clustering analysis is limited and the default one is k-means as compared to the number of packages and functions available in R to perform various types of clustering. I will dedicate a future article to cover cluster and fpc packages in R.
In this case, what options that a Tableau user has for extended/advanced clustering? The answer is R integration by calling R packages from Tableau using similar steps that I explained in this article.