Mastering Machine Learning with R - Second Edition

Distance calculations

As mentioned previously, Euclidean distance is commonly used to build the input for hierarchical clustering. Let's look at a simple example of how to calculate it with two observations and two variables/features.

Let's say that observation A costs $5.00 and weighs 3 pounds. Further, observation B costs $3.00 and weighs 5 pounds. We can place these values in the distance formula: distance between A and B is equal to the square root of the sum of the squared differences, which in our example would be as follows:

d(A, B) = square root((5 - 3)² + (3 - 5)²) , which is equal to 2.83

The value of 2.83 is not a meaningful value in and of itself, but is important in the context of the other pairwise distances. This calculation ...