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Inductive Databases in the Relational Model: The Data as the Bridge

  • Stefan Kramer
  • Volker Aufschild
  • Andreas Hapfelmeier
  • Alexander Jarasch
  • Kristina Kessler
  • Stefan Reckow
  • Jörg Wicker
  • Lothar Richter
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3933)

Abstract

We present a new and comprehensive approach to inductive databases in the relational model. The main contribution is a new inductive query language extending SQL, with the goal of supporting the whole knowledge discovery process, from pre-processing via data mining to post-processing. A prototype system supporting the query language was developed in the SINDBAD (structured inductive database development) project. Setting aside models and focusing on distance-based and instance-based methods, closure can easily be achieved. An example scenario from the area of gene expression data analysis demonstrates the power and simplicity of the concept. We hope that this preliminary work will help to bring the fundamental issues, such as the integration of various pattern domains and data mining techniques, to the attention of the inductive database community.

Keywords

Data Mining Feature Selection Relational Model Query Language Frequent Itemsets 
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.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Stefan Kramer
    • 1
  • Volker Aufschild
    • 1
  • Andreas Hapfelmeier
    • 1
  • Alexander Jarasch
    • 1
  • Kristina Kessler
    • 1
  • Stefan Reckow
    • 1
  • Jörg Wicker
    • 1
  • Lothar Richter
    • 1
  1. 1.Institut für InformatikTechnische Universität MünchenGarching bei MünchenGermany

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