Mining Association Rules for Label Ranking

  • Cláudio Rebelo de Sá
  • Carlos Soares
  • Alípio Mário Jorge
  • Paulo Azevedo
  • Joaquim Costa
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6635)


Recently, a number of learning algorithms have been adapted for label ranking, including instance-based and tree-based methods. In this paper, we propose an adaptation of association rules for label ranking. The adaptation, which is illustrated in this work with APRIORI Algorithm, essentially consists of using variations of the support and confidence measures based on ranking similarity functions that are suitable for label ranking. We also adapt the method to make a prediction from the possibly conflicting consequents of the rules that apply to an example. Despite having made our adaptation from a very simple variant of association rules for classification, the results clearly show that the method is making valid predictions. Additionally, they show that it competes well with state-of-the-art label ranking algorithms.


Association Rule Discretization Method Mining Association Rule Minimum Entropy Support Count 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Cláudio Rebelo de Sá
    • 1
  • Carlos Soares
    • 1
    • 2
  • Alípio Mário Jorge
    • 1
    • 3
  • Paulo Azevedo
    • 5
  • Joaquim Costa
    • 4
  1. 1.LIAAD-INESC Porto L.A.PortoPortugal
  2. 2.Faculdade de EconomiaUniversidade do PortoPortugal
  3. 3.DCC - Faculdade de CienciasUniversidade do PortoPortugal
  4. 4.DM - Faculdade de CienciasUniversidade do PortoPortugal
  5. 5.CCTC, Departamento de InformáticaUniversidade do MinhoPortugal

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