Data Mining: Foundations and Practice

Volume 118 of the series Studies in Computational Intelligence pp 469-484

Data Preprocessing and Data Mining as Generalization

  • Anita WasilewskaAffiliated withDepartment of Computer Science, State University of New York
  • , Ernestina MenasalvasAffiliated withDepartamento de Lenguajes y Sistemas Informaticos Facultad de Informatica, U.P.M

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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.