%0 
%0 Journal Article
%A Pasquier, Nicolas; Taouil, Rafik; Bastide, Yves; Stumme, Gerd & Lakhal, Lotfi
%D 2005
%T Generating a Condensed Representation for Association Rules
%E 
%B Journal Intelligent Information Systems (JIIS)
%C 
%I Kluwer Academic Publishers
%V 24
%6
%N 1
%P 29-60
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%F pasquier2005generating
%K concept, discovery, 2005, association, OntologyHandbook, l3s, analysis, kdd, itemset, myown, rule, data, knowledge, closed, formal, rules, itegpub, condensed, fca, sets, representations, mining
%X 
%Z 
%U http://www.kde.cs.uni-kassel.de/stumme/papers/2005/pasquier2005generating.pdf
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%0 
%0 Journal Article
%A Pasquier, Nicolas; Taouil, Rafik; Bastide, Yves; Stumme, Gerd & Lakhal, Lotfi
%D 2005
%T Generating a Condensed Representation for Association Rules
%E 
%B Journal Intelligent Information Systems (JIIS)
%C 
%I Kluwer Academic Publishers
%V 24
%6
%N 1
%P 29-60
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%2 Publications of Gerd Stumme
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%F pasquier2005generating
%K concept, discovery, 2005, association, OntologyHandbook, l3s, analysis, kdd, itemset, myown, rule, data, knowledge, closed, formal, rules, itegpub, condensed, fca, sets, representations, mining
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%U http://www.kde.cs.uni-kassel.de/stumme/papers/2005/pasquier2005generating.pdf
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%0 
%0 Journal Article
%A Stumme, Gerd; Taouil, Rafik; Bastide, Yves; Pasquier, Nicolas & Lakhal, Lotfi
%D 2002
%T Computing iceberg concept lattices with TITANIC
%E 
%B Data \& Knowledge Engineering
%C 
%I Elsevier Science Publishers B. V.
%V 42
%6
%N 2
%P 189--222
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%F stumme2002computing
%K titanic, itegpub, icfca, l3s, fca, myown, citedBy:doerfel2012publication
%X 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.
%Z 
%U http://portal.acm.org/citation.cfm?id=606457
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%0 
%0 Journal Article
%A Stumme, Gerd; Taouil, Rafik; Bastide, Yves; Pasquier, Nicolas & Lakhal, Lotfi
%D 2002
%T Computing iceberg concept lattices with TITANIC
%E 
%B Data & Knowledge Engineering
%C 
%I Elsevier Science Publishers B. V.
%V 42
%6
%N 2
%P 189--222
%& 
%Y 
%S 
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%8 August
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%F stumme2002computing
%K titanic, concept, iceberg, fca, kdd, computing
%X 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.
%Z 
%U http://portal.acm.org/citation.cfm?id=606457
%+
%^

%0 
%0 Journal Article
%A Stumme, Gerd; Taouil, Rafik; Bastide, Yves; Pasquier, Nicolas & Lakhal, Lotfi
%D 2002
%T Computing iceberg concept lattices with TITANIC
%E 
%B Data \& Knowledge Engineering
%C 
%I Elsevier Science Publishers B. V.
%V 42
%6
%N 2
%P 189--222
%& 
%Y 
%S 
%7
%8 August
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%! 
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%@ 0169-023X
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%2 
%3 article
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%F stumme2002computing
%K lattice, titanic, concept, formal, iceberg, analysis, fca
%X 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.
%Z 
%U http://portal.acm.org/citation.cfm?id=606457
%+
%^

%0 
%0 Journal Article
%A Stumme, Gerd; Taouil, Rafik; Bastide, Yves; Pasquier, Nicolas & Lakhal, Lotfi
%D 2002
%T Computing iceberg concept lattices with TITANIC
%E 
%B Data Knowl. Eng.
%C 
%I Elsevier Science Publishers B. V.
%V 42
%6
%N 2
%P 189--222
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%F 606457
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%0 
%0 Journal Article
%A Pasquier, Nicolas; Bastide, Yves; Taouil, Rafik & Lakhal, Lotfi
%D 1999
%T Efficient mining of association rules using closed itemset lattices 
%E 
%B Information Systems 
%C 
%I 
%V 24
%6
%N 1
%P 25 - 46
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%2 Efficient mining of association rules using closed itemset lattices
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%K association, fca, rule
%X 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. 
%Z 
%U http://www.sciencedirect.com/science/article/pii/S0306437999000034
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%0 
%0 Conference Proceedings
%A Pasquier, Nicolas; Bastide, Yves; Taouil, Rafik & Lakhal, Lotfi
%D 1998
%T Pruning closed itemset lattices for associations rules.
%E Bouzeghoub, Mokrane
%B Bases de Donn�es Avanc�es
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%3 inproceedings
%4 conf/bda/1998
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%F pasquier98pruning
%K rules, closed, concept, formal, association, OntologyHandbook, FCA, analysis, fca, itemset, mining
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%U http://dblp.uni-trier.de/db/conf/bda/bda98.html#PasquierBTL98
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%0 
%0 Conference Proceedings
%A Pasquier, Nicolas; Bastide, Yves; Taouil, Rafik & Lakhal, Lotfi
%D 1998
%T Pruning closed itemset lattices for associations rules.
%E Bouzeghoub, Mokrane
%B Bases de Données Avancées
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%K rules, closed, concept, formal, association, analysis, fca, itemset, mining
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%U http://dblp.uni-trier.de/db/conf/bda/bda98.html#PasquierBTL98
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