Skip to main content

Data Mining as Generalization: A Formal Model

  • 316 Accesses

Part of the Studies in Computational Intelligence book series (SCI,volume 9)

Abstract

The model we present here formalizes the definition of Data Mining as the process of information generalization. In the model the Data Mining algorithms are defined as generalization operators. We show that only three generalizations operators: classification operator, clustering operator, and association operator are needed to express all Data Mining algorithms for classification, clustering, and association, respectively. The framework of the model allows to describe formally the hybrid systems; combination of classifiers into multi-classifiers, and combination of clustering with classification.

Keywords

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

This is a preview of subscription content, log in via an institution.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Author information

Authors and Affiliations

Authors

Editor information

Tsau Young Lin Setsuo Ohsuga Churn-Jung Liau Xiaohua Hu

Rights and permissions

Reprints and permissions

About this chapter

Cite this chapter

Menasalvas1, E., Wasilewska2, A. Data Mining as Generalization: A Formal Model. In: Young Lin, T., Ohsuga, S., Liau, CJ., Hu, X. (eds) Foundations and Novel Approaches in Data Mining. Studies in Computational Intelligence, vol 9. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11539827_6

Download citation

  • DOI: https://doi.org/10.1007/11539827_6

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-28315-7

  • Online ISBN: 978-3-540-31229-1

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics