FUN: An Efficient Algorithm for Mining Functional and Embedded Dependencies

  • Noël Novelli
  • Rosine Cicchetti
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1973)


Discovering functional dependencies from existing databases is an important technique strongly required in database design and administration tools. Investigated for long years, such an issue has been recently addressed with a data mining viewpoint, in a novel and more efficient way by following from principles of level-wise algorithms. In this paper, we propose a new characterization of minimal functional dependencies which provides a formal framework simpler than previous proposals. The algorithm, defined for enforcing our approach has been implemented and experimented. It is more efficient (in whatever configuration of original data) than the best operational solution (according to our knowledge): the algorithm Tane. Moreover, our approach also performs (without additional execution time) the mining of embedded functional dependencies, i.e. dependencies holding for a subset of the attribute set initially considered (e.g. for materialized views widely used in particular for managing data warehouses).


Execution Time Functional Dependency Mining Association Rule Correlation Rate Maximal Subset 
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 2001

Authors and Affiliations

  • Noël Novelli
    • 1
  • Rosine Cicchetti
    • 1
  1. 1.LIM, CNRS FRE-2246Université de la MéditerranéeMarseille Cedex 9France

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