Abstract: A method for interpreting a plurality of m-dimensional attribute vectors (m2) assigned to a plurality of locations in an n-dimensional interpretation space (n1), which method comprises arranging at least a subset of the attribute vectors as points in an m-dimensional attribute space; defining k classes (k2) of attribute vectors by identifying for each class at least one classification point in attribute space; postulating a classification rule for points in attribute space; determining a class-membership attribute of a point in attribute space using the classification points and the classification rule to obtain a classified point; and assigning a display parameter to the classified point which is related to the class-membership attribute. In one embodiment the display parameter is a mixed display parameter derived from probabilistic membership values each representing a probability that the classified point belongs to a selected class.

Abstract: A method for interpreting a plurality of m-dimensional attribute vectors (m2) assigned to a plurality of locations in an n-dimensional interpretation space (n1), which method comprises arranging at least a subset of the attribute vectors as points in an m-dimensional attribute space; defining k classes (k2) of attribute vectors by identifying for each class at least one classification point in attribute space; postulating a classification rule for points in attribute space; determining a class-membership attribute of a point in attribute space using the classification points and the classification rule to obtain a classified point; and assigning a display parameter to the classified point which is related to the class-membership attribute. In one embodiment the display parameter is a mixed display parameter derived from probabilistic membership values each representing a probability that the classified point belongs to a selected class.