Pasquier, N.; Taouil, R.; Bastide, Y.; Stumme, G. & Lakhal, L.: Generating a Condensed Representation for Association Rules. In: *Journal Intelligent Information Systems (JIIS)* 24 (2005), Nr. 1, S. 29-60

[Volltext]

@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},
volume = {24},
number = {1},
pages = {29-60},
url = {http://www.kde.cs.uni-kassel.de/stumme/papers/2005/pasquier2005generating.pdf},
keywords = {concept, discovery, 2005, association, OntologyHandbook, l3s, analysis, kdd, itemset, myown, rule, data, knowledge, closed, formal, rules, itegpub, condensed, fca, sets, representations, mining}
}

Pasquier, N.; Taouil, R.; Bastide, Y.; Stumme, G. & Lakhal, L.: Generating a Condensed Representation for Association Rules. In: *Journal Intelligent Information Systems (JIIS)* 24 (2005), Nr. 1, S. 29-60

[Volltext]

@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},
volume = {24},
number = {1},
pages = {29-60},
url = {http://www.kde.cs.uni-kassel.de/stumme/papers/2005/pasquier2005generating.pdf},
keywords = {concept, discovery, 2005, association, OntologyHandbook, l3s, analysis, kdd, itemset, myown, rule, data, knowledge, closed, formal, rules, itegpub, condensed, fca, sets, representations, mining}
}

Stumme, G.; Taouil, R.; Bastide, Y.; Pasquier, N. & Lakhal, L.: Computing iceberg concept lattices with TITANIC. In: *Data & Knowledge Engineering* 42 (2002), Nr. 2, S. 189-222

[Volltext]

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},
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.}
}

Stumme, G.; Taouil, R.; Bastide, Y.; Pasquier, N. & Lakhal, L.: Computing iceberg concept lattices with TITANIC. In: *Data & Knowledge Engineering* 42 (2002), Nr. 2, S. 189-222

[Volltext]

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},
keywords = {lattice, titanic, concept, formal, iceberg, analysis, fca},
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.}
}

Pasquier, N.; Bastide, Y.; Taouil, R. & Lakhal, L.: Pruning closed itemset lattices for associations rules.. In: Bouzeghoub, M. (Hrsg.): *Bases de Donn�es Avanc�es*. 1998

[Volltext]

@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}
}

Pasquier, N.; Bastide, Y.; Taouil, R. & Lakhal, L.: Pruning closed itemset lattices for associations rules.. In: Bouzeghoub, M. (Hrsg.): *Bases de Données Avancées*. 1998

[Volltext]

@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}
}