This list is not exhaustive. For example, lists
multiple distance and similarity measures for different kinds of data: numerical
(12), boolean (8), string (5), images & color (2), geospatial & temporal (4),
and general & mixed (1).
Nominal variables are variables that have two or more categories, but which do
not have an intrinsic order. Dichotomous variables are nominal variables which
have only two categories.
Dichotomous attributes (e.g. yes-or-no) are distinct from binary attributes
(present vs. absent), e.g. binary attributes may be asymmetric in that
co-presence suggests similarity, but co-absence may or may not be considered
evidence of similarity. .
Nominal variables are variables that have two or more categories, but which do not have an intrinsic order. Dichotomous variables are nominal variables which have only two categories.
Dichotomous attributes (e.g. yes-or-no) are distinct from binary attributes (present vs. absent), e.g. binary attributes may be asymmetric in that co-presence suggests similarity, but co-absence may or may not be considered evidence of similarity. .
...