Euclidean metric: || u || = sqrt( u_{1}^{2} + u_{2}^{2} + ... + u_{d}^{2} ) Manhattan (or taxicab) metric: || u || = |u_{1}| + |u_{2}| + ... + |u_{d}|

Contours of constant Euclidean distance are circles (or spheres) Contours of constant Manhattan distance are squares (or boxes) Contours of constant Mahalanobis distance are ellipses (or ellipsoids)

If Feature i and Feature j tend to increase together, then c(i,j) > 0 If Feature i tends to decrease when Feature j increases, then c(i,j) < 0 If Feature i and Feature j are independent, then c(i,j) = 0 | c(i,j) | <= s(i) s(j), where s(i) is the standard deviation of Feature i c(i,i) = s(i)^{2} = v(i)