Scaled Euclidian distances: a general dissimilarity index with a suitably defined geometrical foundation

摘要

A simple manipulation of the Euclidian distance expression permits to obtain a scaled dissimilarity index measure, varying within a range of values lying in the interval [0,1]. Here is presented the theoretical background, its application to quantum similarity and use for artificial intelligence general purposes as well. The origin of Hodgkin-Richards index is analyzed and compared with quantum similarity Carbó index.