Cluster analysis partitions the marks in the view into clusters, where the marks within each cluster are more similar to one another than they are to marks in other clusters. Tableau distinguishes clusters using color.

### Note

For additional insight into how clustering works in Tableau, see the blog post *Understanding Clustering in Tableau 10* at https://boraberan.wordpress.com/2016/07/19/understanding-clustering-in-tableau-10/.

Tableau uses the k-means algorithm for clustering. For a given number of clusters *k*, the algorithm partitions the data into *k* clusters. Each cluster has a center (centroid) that is the mean value of all the points in that cluster. The k-means locates centers through an iterative procedure that minimizes distances between individual points in a cluster and the cluster center. In Tableau, you can specify a desired number of clusters, or have Tableau test different values of k and suggest an optimal number of clusters...