Affiliations: [a] Instituto de Ciências Matemáticas e de Computação – Universidade de São Paulo (USP), Centro, São Carlos, SP, Brazil | [b] Instituto de Geociências e Ciências Exatas – UNESP – Univ Estadual Paulista, Rio Claro, SP, Brazil
Correspondence:
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Corresponding author: Fabiano Fernandes dos Santos, R. Dr. Carlos de Camargo Salles, 446 Ap 13, Jardim Lutfalla, São Carlos, SP, Brazil. CEP 13560-550; Tel.: +55 16 91738829; Fax: +55 16 33739700; E-mail: fabianof@icmc.usp.br
Abstract: 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 must be built using all the terms in the documents of the collection. This paper presents the SeCLAR method, which explores the use of association rules in the selection of good candidates for labels of hierarchical document clusters. The purpose of this method is to select a subset of terms by exploring the relationship among the terms of each document. Thus, these candidates can be processed by a classical method to generate the labels. An experimental study demonstrates the potential of the proposed approach to improve the precision and recall of labels obtained by classical methods only considering the terms which are potentially more discriminative.
Keywords: Labeling hierarchical clustering, association rules, text mining
