Calculates the absolute magnitude of the differences between Cartesian
coordinates. Also known as maximum value distance. The Chebychev distance
may be appropriate if the difference between points is reflected more by
differences in individual dimensions rather than all the dimensions
considered together.

Note that this distance measurement is very sensitive to outlying
measurements.

Calculates the straight line distance between two Cartesian coordinates.
Unless the exact difference is required, prefer the computationally-cheaper
quadrance instead.

Calculates the distance between two points to be the sum of the absolute
differences of their Cartesian coordinates. It computes the distance that
would be traveled to get from one data point to the other if a grid-like
path is followed.

Quadrance (also called squared euclidean distance) measures separation of
points in Euclidean space. It is not a metric as it does not satisfy the
triangle inequality, however, it is frequently used in optimization problems
in which distances only have to be compared.

The output of Jarvis-Patrick and K-Means clustering is not affected if
Euclidean distance is replaced with Euclidean squared. However, the output
of hierarchical clustering is likely to change.