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Formal Concept Analysis for Knowledge Discovery and Data Mining: The New Challenges

  • Petko Valtchev
  • Rokia Missaoui
  • Robert Godin
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2961)

Abstract

Data mining (DM) is the extraction of regularities from raw data, which are further transformed within the wider process of knowledge discovery in databases (KDD) into non-trivial facts intended to support decision making. Formal concept analysis (FCA) offers an appropriate framework for KDD, whereby our focus here is on its potential for DM support. A variety of mining methods powered by FCA have been published and the figures grow steadily, especially in the association rule mining (ARM) field. However, an analysis of current ARM practices suggests the impact of FCA has not reached its limits, i.e., appropriate FCA-based techniques could successfully apply in a larger set of situations. As a first step in the projected FCA expansion, we discuss the existing ARM methods, provide a set of guidelines for the design of novel ones, and list some open algorithmic issues on the FCA side. As an illustration, we propose two on-line methods computing the minimal generators of a closure system.

Keywords

Data Mining Association Rule Knowledge Discovery Concept Lattice Formal Concept Analysis 
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 2004

Authors and Affiliations

  • Petko Valtchev
    • 1
  • Rokia Missaoui
    • 2
  • Robert Godin
    • 3
  1. 1.DIRO, Université de MontréalMontréal (Qc)Canada
  2. 2.Département d’informatique et d’ingénierieUQOGatineau (Qc)Canada
  3. 3.Département d’informatiqueUQAMMontréal (Qc)Canada

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