I |
Lakhal, L. & Stumme, G.
(2005):
Efficient Mining of Association Rules Based on Formal Concept Analysis.
In: Formal Concept Analysis.
3626. Aufl./Vol..
Hrsg./Editors: Ganter, B.; Stumme, G. & Wille, R.
Verlag/Publisher: Springer Berlin Heidelberg,
Erscheinungsjahr/Year: 2005.
Seiten/Pages: 180-195.
[Volltext] [Kurzfassung] [BibTeX]
[Endnote]
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
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|
Lakhal, L. & Stumme, G.
(2005):
Efficient Mining of Association Rules Based on Formal Concept Analysis. LNAI Heidelberg
[Volltext] [Kurzfassung] [BibTeX] [Endnote] Association rules are a popular knowledge discovery technique for
rehouse basket analysis. They indicate which items of the rehouse are frequently bought together. The problem of association le mining has first been stated in 1993. Five years later, several search groups discovered that this problem has a strong connection Formal Concept Analysis (FCA). In this survey, we will first troduce some basic ideas of this connection along a specific gorithm, and show how FCA helps in reducing the number resulting rules without loss of information, before giving a neral overview over the history and state of the art of applying A for association rule mining.
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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.}
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|
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Lakhal, L. & Stumme, G.
(2005):
Efficient Mining of Association Rules Based on Formal Concept Analysis. LNAI Heidelberg
[Volltext] [Kurzfassung] [BibTeX] [Endnote] Association rules are a popular knowledge discovery technique for
rehouse basket analysis. They indicate which items of the rehouse are frequently bought together. The problem of association le mining has first been stated in 1993. Five years later, several search groups discovered that this problem has a strong connection Formal Concept Analysis (FCA). In this survey, we will first troduce some basic ideas of this connection along a specific gorithm, and show how FCA helps in reducing the number resulting rules without loss of information, before giving a neral overview over the history and state of the art of applying A for association rule mining.
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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.}
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|
|
J |
Pasquier, N.; Taouil, R.; Bastide, Y.; Stumme, G. & Lakhal, L.
(2005):
Generating a Condensed Representation for Association Rules.
In: Journal Intelligent Information Systems (JIIS),
Ausgabe/Number: 1,
Vol. 24,
Verlag/Publisher: Kluwer Academic Publishers.
Erscheinungsjahr/Year: 2005.
Seiten/Pages: 29-60.
[Volltext] [BibTeX]
[Endnote]
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title = {Generating a Condensed Representation for Association Rules},
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publisher = {Kluwer Academic Publishers},
year = {2005},
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%T = Generating a Condensed Representation for Association Rules
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|
J |
Pasquier, N.; Taouil, R.; Bastide, Y.; Stumme, G. & Lakhal, L.
(2005):
Generating a Condensed Representation for Association Rules.
In: Journal Intelligent Information Systems (JIIS),
Ausgabe/Number: 1,
Vol. 24,
Verlag/Publisher: Kluwer Academic Publishers.
Erscheinungsjahr/Year: 2005.
Seiten/Pages: 29-60.
[Volltext] [BibTeX]
[Endnote]
@article{pasquier2005generating,
author = {Pasquier, Nicolas and Taouil, Rafik and Bastide, Yves and Stumme, Gerd and Lakhal, Lotfi},
title = {Generating a Condensed Representation for Association Rules},
journal = {Journal Intelligent Information Systems (JIIS)},
publisher = {Kluwer Academic Publishers},
year = {2005},
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%0 = article
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%D = 2005
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%T = Generating a Condensed Representation for Association Rules
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|
J |
Stumme, G.; Taouil, R.; Bastide, Y.; Pasquier, N. & Lakhal, L.
(2002):
Computing iceberg concept lattices with TITANIC.
In: Data & Knowledge Engineering,
Ausgabe/Number: 2,
Vol. 42,
Verlag/Publisher: Elsevier Science Publishers B. V..
Erscheinungsjahr/Year: 2002.
Seiten/Pages: 189-222.
[Volltext] [Kurzfassung] [BibTeX]
[Endnote]
We introduce the notion of iceberg concept lattices and show their use in knowledge discovery in databases. Iceberg lattices are a conceptual clustering method, which is well suited for analyzing very large databases. They also serve as a condensed representation of frequent itemsets, as starting point for computing bases of association rules, and as a visualization method for association rules. Iceberg concept lattices are based on the theory of Formal Concept Analysis, a mathematical theory with applications in data analysis, information retrieval, and knowledge discovery. We present a new algorithm called TITANIC for computing (iceberg) concept lattices. It is based on data mining techniques with a level-wise approach. In fact, TITANIC can be used for a more general problem: Computing arbitrary closure systems when the closure operator comes along with a so-called weight function. The use of weight functions for computing closure systems has not been discussed in the literature up to now. Applications providing such a weight function include association rule mining, functional dependencies in databases, conceptual clustering, and ontology engineering. The algorithm is experimentally evaluated and compared with Ganter's Next-Closure algorithm. The evaluation shows an important gain in efficiency, especially for weakly correlated data.
@article{stumme2002computing,
author = {Stumme, Gerd and Taouil, Rafik and Bastide, Yves and Pasquier, Nicolas and Lakhal, Lotfi},
title = {Computing iceberg concept lattices with TITANIC},
journal = {Data & Knowledge Engineering},
publisher = {Elsevier Science Publishers B. V.},
address = {Amsterdam, The Netherlands, The Netherlands},
year = {2002},
volume = {42},
number = {2},
pages = {189--222},
url = {http://portal.acm.org/citation.cfm?id=606457},
doi = {10.1016/S0169-023X(02)00057-5},
issn = {0169-023X},
keywords = {titanic, itegpub, icfca, l3s, fca, myown, citedBy:doerfel2012publication},
abstract = {We introduce the notion of iceberg concept lattices and show their use in knowledge discovery in databases. Iceberg lattices are a conceptual clustering method, which is well suited for analyzing very large databases. They also serve as a condensed representation of frequent itemsets, as starting point for computing bases of association rules, and as a visualization method for association rules. Iceberg concept lattices are based on the theory of Formal Concept Analysis, a mathematical theory with applications in data analysis, information retrieval, and knowledge discovery. We present a new algorithm called TITANIC for computing (iceberg) concept lattices. It is based on data mining techniques with a level-wise approach. In fact, TITANIC can be used for a more general problem: Computing arbitrary closure systems when the closure operator comes along with a so-called weight function. The use of weight functions for computing closure systems has not been discussed in the literature up to now. Applications providing such a weight function include association rule mining, functional dependencies in databases, conceptual clustering, and ontology engineering. The algorithm is experimentally evaluated and compared with Ganter's Next-Closure algorithm. The evaluation shows an important gain in efficiency, especially for weakly correlated data.}
}
%0 = article
%A = Stumme, Gerd and Taouil, Rafik and Bastide, Yves and Pasquier, Nicolas and Lakhal, Lotfi
%C = Amsterdam, The Netherlands, The Netherlands
%D = 2002
%I = Elsevier Science Publishers B. V.
%T = Computing iceberg concept lattices with TITANIC
%U = http://portal.acm.org/citation.cfm?id=606457
|
J |
Stumme, G.; Taouil, R.; Bastide, Y.; Pasquier, N. & Lakhal, L.
(2002):
Computing iceberg concept lattices with TITANIC.
In: Data & Knowledge Engineering,
Ausgabe/Number: 2,
Vol. 42,
Verlag/Publisher: Elsevier Science Publishers B. V..
Erscheinungsjahr/Year: 2002.
Seiten/Pages: 189-222.
[Volltext] [Kurzfassung] [BibTeX]
[Endnote]
We introduce the notion of iceberg concept lattices and show their use in knowledge discovery in databases. Iceberg lattices are a conceptual clustering method, which is well suited for analyzing very large databases. They also serve as a condensed representation of frequent itemsets, as starting point for computing bases of association rules, and as a visualization method for association rules. Iceberg concept lattices are based on the theory of Formal Concept Analysis, a mathematical theory with applications in data analysis, information retrieval, and knowledge discovery. We present a new algorithm called TITANIC for computing (iceberg) concept lattices. It is based on data mining techniques with a level-wise approach. In fact, TITANIC can be used for a more general problem: Computing arbitrary closure systems when the closure operator comes along with a so-called weight function. The use of weight functions for computing closure systems has not been discussed in the literature up to now. Applications providing such a weight function include association rule mining, functional dependencies in databases, conceptual clustering, and ontology engineering. The algorithm is experimentally evaluated and compared with Ganter's Next-Closure algorithm. The evaluation shows an important gain in efficiency, especially for weakly correlated data.
@article{stumme2002computing,
author = {Stumme, Gerd and Taouil, Rafik and Bastide, Yves and Pasquier, Nicolas and Lakhal, Lotfi},
title = {Computing iceberg concept lattices with TITANIC},
journal = {Data & Knowledge Engineering},
publisher = {Elsevier Science Publishers B. V.},
address = {Amsterdam, The Netherlands, The Netherlands},
year = {2002},
volume = {42},
number = {2},
pages = {189--222},
url = {http://portal.acm.org/citation.cfm?id=606457},
doi = {10.1016/S0169-023X(02)00057-5},
issn = {0169-023X},
keywords = {titanic, concept, iceberg, fca, kdd, computing},
abstract = {We introduce the notion of iceberg concept lattices and show their use in knowledge discovery in databases. Iceberg lattices are a conceptual clustering method, which is well suited for analyzing very large databases. They also serve as a condensed representation of frequent itemsets, as starting point for computing bases of association rules, and as a visualization method for association rules. Iceberg concept lattices are based on the theory of Formal Concept Analysis, a mathematical theory with applications in data analysis, information retrieval, and knowledge discovery. We present a new algorithm called TITANIC for computing (iceberg) concept lattices. It is based on data mining techniques with a level-wise approach. In fact, TITANIC can be used for a more general problem: Computing arbitrary closure systems when the closure operator comes along with a so-called weight function. The use of weight functions for computing closure systems has not been discussed in the literature up to now. Applications providing such a weight function include association rule mining, functional dependencies in databases, conceptual clustering, and ontology engineering. The algorithm is experimentally evaluated and compared with Ganter's Next-Closure algorithm. The evaluation shows an important gain in efficiency, especially for weakly correlated data.}
}
%0 = article
%A = Stumme, Gerd and Taouil, Rafik and Bastide, Yves and Pasquier, Nicolas and Lakhal, Lotfi
%C = Amsterdam, The Netherlands, The Netherlands
%D = 2002
%I = Elsevier Science Publishers B. V.
%T = Computing iceberg concept lattices with TITANIC
%U = http://portal.acm.org/citation.cfm?id=606457
|
J |
Stumme, G.; Taouil, R.; Bastide, Y.; Pasquier, N. & Lakhal, L.
(2002):
Computing iceberg concept lattices with TITANIC.
In: Data & Knowledge Engineering,
Ausgabe/Number: 2,
Vol. 42,
Verlag/Publisher: Elsevier Science Publishers B. V..
Erscheinungsjahr/Year: 2002.
Seiten/Pages: 189-222.
[Volltext] [Kurzfassung] [BibTeX]
[Endnote]
We introduce the notion of iceberg concept lattices and show their use in knowledge discovery in databases. Iceberg lattices are a conceptual clustering method, which is well suited for analyzing very large databases. They also serve as a condensed representation of frequent itemsets, as starting point for computing bases of association rules, and as a visualization method for association rules. Iceberg concept lattices are based on the theory of Formal Concept Analysis, a mathematical theory with applications in data analysis, information retrieval, and knowledge discovery. We present a new algorithm called TITANIC for computing (iceberg) concept lattices. It is based on data mining techniques with a level-wise approach. In fact, TITANIC can be used for a more general problem: Computing arbitrary closure systems when the closure operator comes along with a so-called weight function. The use of weight functions for computing closure systems has not been discussed in the literature up to now. Applications providing such a weight function include association rule mining, functional dependencies in databases, conceptual clustering, and ontology engineering. The algorithm is experimentally evaluated and compared with Ganter's Next-Closure algorithm. The evaluation shows an important gain in efficiency, especially for weakly correlated data.
@article{stumme2002computing,
author = {Stumme, Gerd and Taouil, Rafik and Bastide, Yves and Pasquier, Nicolas and Lakhal, Lotfi},
title = {Computing iceberg concept lattices with TITANIC},
journal = {Data & Knowledge Engineering},
publisher = {Elsevier Science Publishers B. V.},
address = {Amsterdam, The Netherlands, The Netherlands},
year = {2002},
volume = {42},
number = {2},
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abstract = {We introduce the notion of iceberg concept lattices and show their use in knowledge discovery in databases. Iceberg lattices are a conceptual clustering method, which is well suited for analyzing very large databases. They also serve as a condensed representation of frequent itemsets, as starting point for computing bases of association rules, and as a visualization method for association rules. Iceberg concept lattices are based on the theory of Formal Concept Analysis, a mathematical theory with applications in data analysis, information retrieval, and knowledge discovery. We present a new algorithm called TITANIC for computing (iceberg) concept lattices. It is based on data mining techniques with a level-wise approach. In fact, TITANIC can be used for a more general problem: Computing arbitrary closure systems when the closure operator comes along with a so-called weight function. The use of weight functions for computing closure systems has not been discussed in the literature up to now. Applications providing such a weight function include association rule mining, functional dependencies in databases, conceptual clustering, and ontology engineering. The algorithm is experimentally evaluated and compared with Ganter's Next-Closure algorithm. The evaluation shows an important gain in efficiency, especially for weakly correlated data.}
}
%0 = article
%A = Stumme, Gerd and Taouil, Rafik and Bastide, Yves and Pasquier, Nicolas and Lakhal, Lotfi
%C = Amsterdam, The Netherlands, The Netherlands
%D = 2002
%I = Elsevier Science Publishers B. V.
%T = Computing iceberg concept lattices with TITANIC
%U = http://portal.acm.org/citation.cfm?id=606457
|
J |
Stumme, G.; Taouil, R.; Bastide, Y.; Pasquier, N. & Lakhal, L.
(2002):
Computing iceberg concept lattices with TITANIC.
In: Data Knowl. Eng.,
Ausgabe/Number: 2,
Vol. 42,
Verlag/Publisher: Elsevier Science Publishers B. V..
Erscheinungsjahr/Year: 2002.
Seiten/Pages: 189-222.
[BibTeX]
[Endnote]
@article{606457,
author = {Stumme, Gerd and Taouil, Rafik and Bastide, Yves and Pasquier, Nicolas and Lakhal, Lotfi},
title = {Computing iceberg concept lattices with TITANIC},
journal = {Data Knowl. Eng.},
publisher = {Elsevier Science Publishers B. V.},
address = {Amsterdam, The Netherlands, The Netherlands},
year = {2002},
volume = {42},
number = {2},
pages = {189--222},
doi = {http://dx.doi.org/10.1016/S0169-023X(02)00057-5},
issn = {0169-023X},
keywords = {fca}
}
%0 = article
%A = Stumme, Gerd and Taouil, Rafik and Bastide, Yves and Pasquier, Nicolas and Lakhal, Lotfi
%C = Amsterdam, The Netherlands, The Netherlands
%D = 2002
%I = Elsevier Science Publishers B. V.
%T = Computing iceberg concept lattices with TITANIC
|
J |
Pasquier, N.; Bastide, Y.; Taouil, R. & Lakhal, L.
(1999):
Efficient mining of association rules using closed itemset lattices .
In: Information Systems ,
Ausgabe/Number: 1,
Vol. 24,
Erscheinungsjahr/Year: 1999.
Seiten/Pages: 25 - 46.
[Volltext] [Kurzfassung] [BibTeX]
[Endnote]
Discovering association rules is one of the most important task in data mining. Many efficient algorithms have been proposed in the literature. The most noticeable are Apriori, Mannila's algorithm, Partition, Sampling and DIC, that are all based on the Apriori mining method: pruning the subset lattice (itemset lattice). In this paper we propose an efficient algorithm, called Close, based on a new mining method: pruning the closed set lattice (closed itemset lattice). This lattice, which is a sub-order of the subset lattice, is closely related to Wille's concept lattice in formal concept analysis. Experiments comparing Close to an optimized version of Apriori showed that Close is very efficient for mining dense and/or correlated data such as census style data, and performs reasonably well for market basket style data.
@article{pasquier1999efficient,
author = {Pasquier, Nicolas and Bastide, Yves and Taouil, Rafik and Lakhal, Lotfi},
title = {Efficient mining of association rules using closed itemset lattices },
journal = {Information Systems },
year = {1999},
volume = {24},
number = {1},
pages = {25 - 46},
url = {http://www.sciencedirect.com/science/article/pii/S0306437999000034},
doi = {http://dx.doi.org/10.1016/S0306-4379(99)00003-4},
issn = {0306-4379},
keywords = {association, fca, rule},
abstract = {Discovering association rules is one of the most important task in data mining. Many efficient algorithms have been proposed in the literature. The most noticeable are Apriori, Mannila's algorithm, Partition, Sampling and DIC, that are all based on the Apriori mining method: pruning the subset lattice (itemset lattice). In this paper we propose an efficient algorithm, called Close, based on a new mining method: pruning the closed set lattice (closed itemset lattice). This lattice, which is a sub-order of the subset lattice, is closely related to Wille's concept lattice in formal concept analysis. Experiments comparing Close to an optimized version of Apriori showed that Close is very efficient for mining dense and/or correlated data such as census style data, and performs reasonably well for market basket style data. }
}
%0 = article
%A = Pasquier, Nicolas and Bastide, Yves and Taouil, Rafik and Lakhal, Lotfi
%D = 1999
%T = Efficient mining of association rules using closed itemset lattices
%U = http://www.sciencedirect.com/science/article/pii/S0306437999000034
|
P |
Pasquier, N.; Bastide, Y.; Taouil, R. & Lakhal, L.
(1998):
Pruning closed itemset lattices for associations rules..
In: Bases de Donn�es Avanc�es,
[Volltext]
[BibTeX][Endnote]
@inproceedings{pasquier98pruning,
author = {Pasquier, Nicolas and Bastide, Yves and Taouil, Rafik and Lakhal, Lotfi},
title = {Pruning closed itemset lattices for associations rules.},
editor = {Bouzeghoub, Mokrane},
booktitle = {Bases de Donn�es Avanc�es},
year = {1998},
url = {http://dblp.uni-trier.de/db/conf/bda/bda98.html#PasquierBTL98},
keywords = {rules, closed, concept, formal, association, OntologyHandbook, FCA, analysis, fca, itemset, mining}
}
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%A = Pasquier, Nicolas and Bastide, Yves and Taouil, Rafik and Lakhal, Lotfi
%B = Bases de Donn�es Avanc�es
%D = 1998
%T = Pruning closed itemset lattices for associations rules.
%U = http://dblp.uni-trier.de/db/conf/bda/bda98.html#PasquierBTL98
|
P |
Pasquier, N.; Bastide, Y.; Taouil, R. & Lakhal, L.
(1998):
Pruning closed itemset lattices for associations rules..
In: Bases de Données Avancées,
[Volltext]
[BibTeX][Endnote]
@inproceedings{pasquier98pruning,
author = {Pasquier, Nicolas and Bastide, Yves and Taouil, Rafik and Lakhal, Lotfi},
title = {Pruning closed itemset lattices for associations rules.},
editor = {Bouzeghoub, Mokrane},
booktitle = {Bases de Données Avancées},
year = {1998},
url = {http://dblp.uni-trier.de/db/conf/bda/bda98.html#PasquierBTL98},
keywords = {rules, closed, concept, formal, association, analysis, fca, itemset, mining}
}
%0 = inproceedings
%A = Pasquier, Nicolas and Bastide, Yves and Taouil, Rafik and Lakhal, Lotfi
%B = Bases de Données Avancées
%D = 1998
%T = Pruning closed itemset lattices for associations rules.
%U = http://dblp.uni-trier.de/db/conf/bda/bda98.html#PasquierBTL98
|