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In machine learning, distance-based algorithms, and other approaches, useinformation that is represented by propositional data. However, this kind ofrepresentation can be quite restrictive and, in many cases, it requires morecomplex structures in order to represent data in a more natural way. Terms arethe basis for functional and logic programming representation. Distancesbetween terms are a useful tool not only to compare terms, but also todetermine the search space in many of these applications. This dissertationapplies distances between terms, exploiting the features of each distance andthe possibility to compare from propositional data types to hierarchicalrepresentations. The distances between terms are applied through the k-NN(k-nearest neighbor) classification algorithm using XML as a common languagerepresentation. To be able to represent these data in an XML structure and totake advantage of the benefits of distance between terms, it is necessary toapply some transformations. These transformations allow the conversion of flatdata into hierarchical data represented in XML, using some techniques based onintuitive associations between the names and values of variables andassociations based on attribute similarity.  Several experiments with the distances between terms of Nienhuys-Cheng andEstruch et al. were performed. In the case of originally propositional data,these distances are compared to the Euclidean distance. In all cases, theexperiments were performed with the distance-weighted k-nearest neighboralgorithm, using several exponents for the attraction function (weighteddistance). It can be seen that in some cases, the term distances cansignificantly improve the results on approaches applied to flatrepresentations.

Application of distances between terms for flat and hierarchical data