Efficient Mining of Association Rules Based on Formal Concept Analysis
L. Lakhal, и G. Stumme. том 3626 из LNAI, стр. 180-195. Springer, Heidelberg, (2005)
Аннотация
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, \titanic, 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.
%0 Book Section
%1 lakhal2005efficient
%A Lakhal, Lotfi
%A Stumme, Gerd
%B Formal Concept Analysis: Foundations and Applications
%C Heidelberg
%D 2005
%E Ganter, Bernhard
%E Stumme, Gerd
%E Wille, Rudolf
%I Springer
%K 2005 analysis association book closed concept condensed data discovery fca formal itegpub itemsets kdd knowledge l3s mining myown representations rules
%P 180-195
%T Efficient Mining of Association Rules Based on Formal Concept Analysis
%U http://www.kde.cs.uni-kassel.de/stumme/papers/2005/lakhal2005efficient.pdf
%V 3626
%X 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, \titanic, 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.
@inbook{lakhal2005efficient,
abstract = {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, \titanic, 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.},
added-at = {2009-07-03T00:14:04.000+0200},
address = {Heidelberg},
author = {Lakhal, Lotfi and Stumme, Gerd},
biburl = {https://www.bibsonomy.org/bibtex/22b350f817428e4c6c7259cd279815091/stumme},
booktitle = {Formal Concept Analysis: Foundations and Applications},
editor = {Ganter, Bernhard and Stumme, Gerd and Wille, Rudolf},
ee = {http://dx.doi.org/10.1007/11528784_10},
interhash = {f5777a0f9dccfcf4f9968119d77297fc},
intrahash = {2b350f817428e4c6c7259cd279815091},
keywords = {2005 analysis association book closed concept condensed data discovery fca formal itegpub itemsets kdd knowledge l3s mining myown representations rules},
pages = {180-195},
publisher = {Springer},
series = {LNAI},
timestamp = {2009-07-03T00:14:04.000+0200},
title = {Efficient Mining of Association Rules Based on Formal Concept Analysis},
url = {http://www.kde.cs.uni-kassel.de/stumme/papers/2005/lakhal2005efficient.pdf},
volume = 3626,
year = 2005
}