When you use Chernoff Faces or parallel coordinates, your main goal is to reduce. You want to find groups within the dataset or population. The challenge is that you don’t always know where to start looking in the faces or the connecting lines, so it’d be nice if you could cluster objects, based on several criteria. This is one of the goals of multidimensional scaling (MDS). Take everything into account, and then place units that are more similar closer together on a plot.
Entire books are written on this topic, so explanations can get technical, but for the sake of simplicity, I’ll keep it at a high level and leave the math for another day. That said, MDS is one of the first concepts I learned in graduate school, and it is worth learning the mechanics behind it, if you’re into that sort of thing.
For more details on the method, look up multidimensional scaling or principal components analysis.
Imagine that you’re in an empty, square-shaped room and there are two other people there. It’s your job to tell those people where to stand in the room, based on their height. The more similar their height, the closer they should stand, and the more different their height, the farther away they should stand. One is really short. The other is really tall. Where should they go? The two people should stand at opposite corners, because they are complete opposites.
Now a third person comes in, and he’s medium height. Sticking with the arrangement scheme, the new person should ...