[vomjom]

There are far better methods than the one linked in the article.

You can train a covariance matrix such that you can get a better distance metric. Particularly, you would use the Mahalanobis Distance:

http://en.wikipedia.org/wiki/Mahalanobis_distance

For classification tasks, there are two good ways of training a covariance matrix for distance metrics: neighborhood components analysis and large margin nearest neighbors.

The effect in the article is just a particular quirk of using the euclidean distance. You could, for example, get the same result by using a 1-norm distance.