Summary
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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Wasilewska, A., Menasalvas, E. (2008). Data Preprocessing and Data Mining as Generalization. In: Lin, T.Y., Xie, Y., Wasilewska, A., Liau, CJ. (eds) Data Mining: Foundations and Practice. Studies in Computational Intelligence, vol 118. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-78488-3_27
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DOI: https://doi.org/10.1007/978-3-540-78488-3_27
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-78487-6
Online ISBN: 978-3-540-78488-3
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