Curse of Dimensionality

The insight behind the term coined by Richard E. Bellman is that with higher dimensions the data distribution become sparser.

To give a concrete example: a data set with 100 rows and 2 properties is denser in 2 dimensional space, than a data set with 100 rows and 3 properties is in 3 dimensional space.