02049nam a2200313 a 450000100080000000500110000800800410001902000220006010000170008224500880009926001490018730000160033650000140035252010410036665000210140765000260142865000140145465300290146865300240149765300210152165300240154265300280156665300260159465300340162065300270165465300160168165300190169770000190171618756482020-01-14       2010    bl uuuu     u00u1 u    #d  a978-0-88986-817-51 aMOURA, M. F.  aA simple method for labeling hierarchical document clusters.h[electronic resource]  aIn: IASTED INTERNATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE AND APPLICATIONS, 10., 2010, Innsbruck. Proceedings... Anaheim: ACTA Press.c2010  ap. 363-371.  aAIA 2010.  aOne of the problems of automatic models in generating topic taxonomies is the process of creating the most significant word list that discriminates each document group. A proposal for doing this through labeling hierarchical document clusters is presented in this paper. The goal of this method is to aid the construction of topic taxonomies. It aims to be language independent and to produce discriminative labels for each group, without replications of the terms along the hierarchy branches. Moreover the method is cluster independent, that is, it can be applied to any hierarchical cluster result or any hierarchy, even those that are manually produced. In order to reach the goals the method is based on a formal definition of a topic taxonomy, also presented in the paper. The method was implemented and evaluated against three other literature methods through their recalling results in a particular information retrieval process. The proposed method outperformed the other three methods; additionally it has a linear complexity.  aCluster analysis  aInformation retrieval  aTaxonomia  aAgrupamento hierárquico  aAttribute selection  aCluster labeling  aDocument clustering  aHierarchical clustering  aMineração de textos  aRecuperação da informação  aSeleção de atributos  aText mining  aTopic taxonomy1 aREZENDE, S. O.