Tableau's power has always been its user-focused flexibility, and working with the user in order to achieve insights at the speed of thought. Tableau's clustering functionality continues the tradition of putting the user front-and-center of the analytics process. So, for example, Tableau allows us to quickly customize geographical areas, for example, which in turn can yield new insights and patterns held within the groups.
Tableau 10.0 comes with k-means clustering as a built-in function. K-means is a popular clustering algorithm that is useful, easy to implement, and it can be faster than some other clustering methods, particularly in the case of big datasets.
We can see the data being grouped, or clustered, around centers. The algorithm works firstly by choosing the cluster centers randomly. Then, it works out the nearest cluster centers, and arranges the data points around it.
K-means then works out the actual cluster center. It then reassigns the data points to the...
