Affiliations: Dipartimento di Informatica, Università degli Studi, via Orabona, 4, 70125 Bari, Italy. E-mail: [email protected], [email protected], [email protected], [email protected] | [x] Faculdade de Economia, University of Porto, Rua Dr. Roberto Frias, 4200-464 Porto, Portugal. E-mail: [email protected] | [y] Institut d'Informatique, Facultés Universitaires Notre Dame de la Paix, Rue Grandgagnage, 21, B-5000 Namur, Belgium. E-mail: [email protected]
Abstract: Symbolic data analysis aims at generalizing some standard statistical data mining methods, such as those developed for classification tasks, to the case of symbolic objects (SOs). These objects synthesize information concerning a group of individuals of a population, eventually stored in a relational database, and ensure confidentiality of original data. Classifying SOs is an important task in symbolic data analysis. In this paper a lazy-learning approach that extends a traditional distance weighted k-Nearest Neighbor classification algorithm to SOs, is presented. The proposed method has been implemented in the system SO-NN (Symbolic Objects Nearest Neighbor) and evaluated on symbolic datasets.
