Efficient Mining of Association Rules Based on Formal Concept Analysis.
2005.
Lotfi Lakhal und Gerd Stumme.
[doi]
[Kurzfassung]
[BibTeX]
Association rules are a popular knowledge discovery technique for
warehouse basket analysis. They indicate which items of the
warehouse are frequently bought together. The problem of association
rule mining has first been stated in 1993. Five years later, several
research groups discovered that this problem has a strong connection
to Formal Concept Analysis (FCA). In this survey, we will first
introduce some basic ideas of this connection along a specific
algorithm, and show how FCA helps in reducing the number
of resulting rules without loss of information, before giving a
general overview over the history and state of the art of applying
FCA for association rule mining.
Generating a Condensed Representation for Association Rules.
Journal Intelligent Information Systems (JIIS), 24(1):29-60, 2005.
Nicolas Pasquier, Rafik Taouil, Yves Bastide, Gerd Stumme und Lotfi Lakhal.
[doi]
[BibTeX]
Intelligent Structuring and Reducing of Association Rules and with Formal Concept Analysis.
In: F. Baader, G. Brewker und T. Eiter
(Herausgeber):
KI 2001: Advances in Artificial Intelligence. KI 2001, Band 2174, Reihe LNAI, Seiten 335-350.
Springer, Heidelberg, 2001.
G. Stumme, R. Taouil, Y. Bastide, N. Pasquier und L. Lakhal.
[doi]
[BibTeX]
Levelwise Search of Frequent Patterns.
In:
Actes des 16ièmes Journées Bases de Données Avancées, Seiten 307-322.
Blois, France, 2000.
Y. Bastide, R. Taouil, N. Pasquier, G. Stumme und L. Lakhal.
[doi]
[BibTeX]
Conceptual Knowledge Discovery with Frequent Concept Lattices.
FB4-Preprint 2043, TU Darmstadt, 1999.
G. Stumme.
[doi]
[BibTeX]