  In machine learning, distance-based algorithms, and other approaches, use
information that is represented by propositional data. However, this kind of
representation can be quite restrictive and, in many cases, it requires more
complex structures in order to represent data in a more natural way. Terms are
the basis for functional and logic programming representation. Distances
between terms are a useful tool not only to compare terms, but also to
determine the search space in many of these applications. This dissertation
applies distances between terms, exploiting the features of each distance and
the possibility to compare from propositional data types to hierarchical
representations. The distances between terms are applied through the k-NN
(k-nearest neighbor) classification algorithm using XML as a common language
representation. To be able to represent these data in an XML structure and to
take advantage of the benefits of distance between terms, it is necessary to
apply some transformations. These transformations allow the conversion of flat
data into hierarchical data represented in XML, using some techniques based on
intuitive associations between the names and values of variables and
associations based on attribute similarity.
  Several experiments with the distances between terms of Nienhuys-Cheng and
Estruch et al. were performed. In the case of originally propositional data,
these distances are compared to the Euclidean distance. In all cases, the
experiments were performed with the distance-weighted k-nearest neighbor
algorithm, using several exponents for the attraction function (weighted
distance). It can be seen that in some cases, the term distances can
significantly improve the results on approaches applied to flat
representations.
