Commonly used default distance metric, performs well in general
Also used in K-means clustering
Also called l2 distance?
Calculated by summing the absolute value of the difference between the dimensions
Ex. In a map, if the Euclidean distance is the shortest route between two points, the Manhattan distance implies moving straight, first along one axis and then along the other — as a car in the city would, reaching a destination by driving along city blocks.
l1 distance is often good for sparse features, or sparse noise: i.e. many of the features are zero, as in text mining using occurrences of rare words