Selecting candidate labels for hierarchical document clusters using association rules

• Autor(a) principal: Dos Santos, Fabiano Fernandes
• Data de Publicação: 2010
• Outros Autores: De Carvalho, Veronica Oliveira [UNESP], Oliveira Rezende, Solange
• Tipo de documento: Artigo de conferência
• Idioma: eng
• Título da fonte: Repositório Institucional da UNESP
• Texto Completo: http://dx.doi.org/10.1007/978-3-642-16773-7_14; http://hdl.handle.net/11449/72231
• topic: association rules; label hierarchical clustering; text mining; Classical methods; Hierarchical document; Precision and recall; Search and retrieval; Structural representation; Text mining; Artificial intelligence; Knowledge representation; Soft computing; Association rules

One way to organize knowledge and make its search and retrieval easier is to create a structural representation divided by hierarchically related topics. Once this structure is built, it is necessary to find labels for each of the obtained clusters. In many cases the labels have to be built using only the terms in the documents of the collection. This paper presents the SeCLAR (Selecting Candidate Labels using Association Rules) method, which explores the use of association rules for the selection of good candidates for labels of hierarchical document clusters. The candidates are processed by a classical method to generate the labels. The idea of the proposed method is to process each parent-child relationship of the nodes as an antecedent-consequent relationship of association rules. The experimental results show that the proposed method can improve the precision and recall of labels obtained by classical methods. © 2010 Springer-Verlag.