Bayesian Networks to Predict Data Mining Algorithm Behavior in Ubiquitous Computing Environments

  • Aysegul Cayci
  • Santiago Eibe
  • Ernestina Menasalvas
  • Yucel Saygin
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6904)


The growing demand of data mining services for ubiquitous computing environments necessitates deployment of appropriate mechanisms that make use of circumstantial factors to adapt the data mining behavior. Despite the efforts and results so far for efficient parameter tuning, incorporating dynamically changing context information on the parameter setting decision is lacking in the present work. Thus, Bayesian networks are used to learn, in possible situations the effects of data mining algorithm parameters on the final model obtained. Based on this knowledge, we propose to infer future algorithm configurations appropriate for situations. Instantiation of the approach for association rules is also shown in the paper and the feasibility of the approach is validated by the experimentation.


automatic data mining data mining configuration ubiquitous data mining 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Aysegul Cayci
    • 1
    • 2
  • Santiago Eibe
    • 1
    • 2
  • Ernestina Menasalvas
    • 1
    • 2
  • Yucel Saygin
    • 1
    • 2
  1. 1.Sabanci UniversityIstanbulTurkey
  2. 2.Facultad de InformaticaUniversidad PolitecnicaMadridSpain

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