Semi-Supervised Learning to Support the Exploration of Association Rules

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8646)


In the last years, many approaches for post-processing association rules have been proposed. The automatics are simple to use, but they don’t consider users’ subjectivity. Unlike, the approaches that consider subjectivity need an explicit description of the users’ knowledge and/or interests, requiring a considerable time from the user. Looking at the problem from another perspective, post-processing can be seen as a classification task, in which the user labels some rules as interesting [I] or not interesting [NI], for example, in order to propagate these labels to the other unlabeled rules. This work presents a framework for post-processing association rules that uses semi-supervised learning in which: (a) the user is constantly directed to the [I] patterns of the domain, minimizing his exploration effort by reducing the exploration space, since his knowledge and/or interests are iteratively propagated; (b) the users’ subjectivity is considered without using any formalism, making the task simpler.


Association Rules Post-processing Semi-supervised Learning (SSL) 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Mansingh, G., Osei-Bryson, K., Reichgelt, H.: Using ontologies to facilitate post-processing of association rules by domain experts. Information Sciences 181(3), 419–434 (2011)CrossRefGoogle Scholar
  2. 2.
    Marinica, C., Guillet, F.: Knowledge-based interactive postmining of association rules using ontologies. IEEE TKDE 22(6), 784–797 (2010)Google Scholar
  3. 3.
    Guillet, F., Hamilton, H.J.: Quality Measures in Data Mining. SCI, vol. 43. Springer, Heidelberg (2007)CrossRefzbMATHGoogle Scholar
  4. 4.
    Ayres, R.M.J., Santos, M.T.P.: Mining generalized association rules using fuzzy ontologies with context-based similarity. In: Proceedings of the 14th ICEIS, vol. 1, pp. 74–83 (2012)Google Scholar
  5. 5.
    Carvalho, V.O., Rezende, S.O., Castro, M.: Obtaining and evaluating generalized association rules. In: Proceedings of the 9th ICEIS, vol. 2, pp. 310–315 (2007)Google Scholar
  6. 6.
    de Carvalho, V.O., dos Santos, F.F., Rezende, S.O., de Padua, R.: PAR-COM: A new methodology for post-processing association rules. In: Zhang, R., Zhang, J., Zhang, Z., Filipe, J., Cordeiro, J. (eds.) ICEIS 2011. LNBIP, vol. 102, pp. 66–80. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  7. 7.
    Berrado, A., Runger, G.C.: Using metarules to organize and group discovered association rules. Data Mining and Knowledge Discovery 14(3), 409–431 (2007)CrossRefMathSciNetGoogle Scholar
  8. 8.
    Zhu, X., Goldberg, A.B.: Introduction to Semi-Supervised Learning, vol. (6). Morgan & Claypool Publishers (2009)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.Instituto de Geociências e Ciências ExatasUNESP - Univ Estadual PaulistaRio ClaroBrazil
  2. 2.Instituto de Ciências Matemáticas e de ComputaçãoUSP - Universidade de São PauloSão CarlosBrazil

Personalised recommendations