%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 citedBy:doerfel2012publication fca icfca itegpub l3s myown 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.