Selecting Candidate Labels for Hierarchical Document Clusters Using Association Rules

  • Fabiano Fernandes dos Santos
  • Veronica Oliveira de Carvalho
  • Solange Oliveira Rezende
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6438)


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.


label hierarchical clustering association rules text mining 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Fabiano Fernandes dos Santos
    • 1
  • Veronica Oliveira de Carvalho
    • 2
  • Solange Oliveira Rezende
    • 1
  1. 1.Instituto de Ciências Matemáticas e de ComputaçãoUniversidade de São Paulo (USP)Brazil
  2. 2.Instituto de Geociências e Ciências Exatas UNESPUniv Estadual PaulistaBrazil

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