Data Mining: Foundations and Practice pp 469-484

Part of the Studies in Computational Intelligence book series (SCI, volume 118)

Data Preprocessing and Data Mining as Generalization

  • Anita Wasilewska
  • Ernestina Menasalvas


We present here an abstract model in which data preprocessing and data mining proper stages of the Data Mining process are are described as two different types of generalization. In the model the data mining and data preprocessing algorithms are defined as certain generalization operators. We use our framework to show that only three Data Mining operators: classification, clustering, and association operator are needed to express all Data Mining algorithms for classification, clustering, and association, respectively. We also are able to show formally that the generalization that occurs in the preprocessing stage is different from the generalization inherent to the data mining proper stage.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Anita Wasilewska
    • 1
  • Ernestina Menasalvas
    • 2
  1. 1.Department of Computer ScienceState University of New YorkStony BrookUSA
  2. 2.Departamento de Lenguajes y Sistemas Informaticos Facultad de InformaticaU.P.MMadridSpain

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