%0 Journal Article
%1 stumme2002computing
%A Stumme, Gerd
%A Taouil, Rafik
%A Bastide, Yves
%A Pasquier, Nicolas
%A Lakhal, Lotfi
%C Amsterdam, The Netherlands, The Netherlands
%D 2002
%I Elsevier Science Publishers B. V.
%J Data & Knowledge Engineering
%K computing concept fca iceberg kdd titanic
%N 2
%P 189--222
%T Computing iceberg concept lattices with TITANIC
%U http://portal.acm.org/citation.cfm?id=606457
%V 42
%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.