@article{stumme2002computing,
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.},
address = {Amsterdam, The Netherlands, The Netherlands},
author = {Stumme, Gerd and Taouil, Rafik and Bastide, Yves and Pasquier, Nicolas and Lakhal, Lotfi},
doi = {10.1016/S0169-023X(02)00057-5},
interhash = {d500ac8a249ca8bf0fb05f382799d48f},
intrahash = {fc31933f0eec502e305b6aecb9ef6e8a},
issn = {0169-023X},
journal = {Data & Knowledge Engineering},
month = aug,
number = 2,
pages = {189--222},
publisher = {Elsevier Science Publishers B. V.},
title = {Computing iceberg concept lattices with TITANIC},
url = {http://portal.acm.org/citation.cfm?id=606457},
volume = 42,
year = 2002
}
@article{stumme2002computing,
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.},
address = {Amsterdam, The Netherlands, The Netherlands},
author = {Stumme, Gerd and Taouil, Rafik and Bastide, Yves and Pasquier, Nicolas and Lakhal, Lotfi},
doi = {10.1016/S0169-023X(02)00057-5},
interhash = {d500ac8a249ca8bf0fb05f382799d48f},
intrahash = {fc31933f0eec502e305b6aecb9ef6e8a},
issn = {0169-023X},
journal = {Data \& Knowledge Engineering},
month = aug,
number = 2,
pages = {189--222},
privnote = {for your eyes only},
publisher = {Elsevier Science Publishers B. V.},
title = {Computing iceberg concept lattices with TITANIC},
url = {http://portal.acm.org/citation.cfm?id=606457},
volume = 42,
year = 2002
}