Data Preprocessing and Data Mining as Generalization
 Anita Wasilewska,
 Ernestina Menasalvas
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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.
 Title
 Data Preprocessing and Data Mining as Generalization
 Book Title
 Data Mining: Foundations and Practice
 Pages
 pp 469484
 Copyright
 2008
 DOI
 10.1007/9783540784883_27
 Print ISBN
 9783540784876
 Online ISBN
 9783540784883
 Series Title
 Studies in Computational Intelligence
 Series Volume
 118
 Series ISSN
 1860949X
 Publisher
 Springer Berlin Heidelberg
 Copyright Holder
 Springer Berlin Heidelberg
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 Editors

 Dr. Tsau Young Lin ^{(2)}
 Dr. Ying Xie ^{(3)}
 Dr. Anita Wasilewska ^{(4)}
 Dr. ChurnJung Liau ^{(5)}
 Editor Affiliations

 2. Department of Computer Science, San Jose State University
 3. Department of Computer Science and Information Systems, Kennesaw State University
 4. Department of Computer Science, The University at Stony Brook
 5. Institute of Information Science, Academia Sinica
 Authors

 Anita Wasilewska ^{(6)}
 Ernestina Menasalvas ^{(7)}
 Author Affiliations

 6. Department of Computer Science, State University of New York, Stony Brook, NY, USA
 7. Departamento de Lenguajes y Sistemas Informaticos Facultad de Informatica, U.P.M, Madrid, Spain
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