Often we are required to quickly locate distinct and well separated groups in our data, for example, grouping customers who have the same buying patterns, or patients with similar symptoms, and so on. More often than not, this can be done using the grouping functionality that we saw in previous chapters.
However, this can be challenging, as finding patterns via manual inspection for complex and distributed datasets with no obvious patterns can be very tough.
The new clustering functionality in Tableau automatically groups together similar data points by finds patterns in data using a K-means algorithm to help the user explore patterns in the data that would be tough to pick out otherwise.
Let us explore the clustering functionality in more detail in the recipe.
We will use a new dataset for the following recipe. The dataset is a .tde, file which has been uploaded on the following link:
https://1drv.ms/u/s!Av5QCoyLTBpnhks3n2mxItiI7-tb.
The file...
