Formal Concept Analysis
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 Journal Article
%1 LotfiLakhal&GerdStumme_2005
%A Lakhal, Lotfi
%A Stumme, Gerd
%D 2005
%K imported
%N LNAI 3626
%T Efficient Mining of Association Rules Based on Formal Concept Analysis
%X Formal Concept Analysis
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.
@article{LotfiLakhal&GerdStumme_2005,
abstract = {Formal Concept Analysis
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 = {2013-08-04T13:35:40.000+0200},
author = {Lakhal, Lotfi and Stumme, Gerd},
biburl = {https://www.bibsonomy.org/bibtex/2b93e67672f9d8ad779567366b4733f43/francesco.k},
interhash = {f5777a0f9dccfcf4f9968119d77297fc},
intrahash = {b93e67672f9d8ad779567366b4733f43},
keywords = {imported},
number = {LNAI 3626},
timestamp = {2013-08-04T14:07:27.000+0200},
title = {Efficient Mining of Association Rules Based on Formal Concept Analysis},
urldate = {15.07.2012},
year = 2005
}