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%0 Journal Article
%A Stumme, Gerd; Taouil, Rafik; Bastide, Yves; Pasquier, Nicolas & Lakhal, Lotfi
%D 2002
%T Computing iceberg concept lattices with TITANIC
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%B Data \& Knowledge Engineering
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%I Elsevier Science Publishers B. V.
%V 42
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%N 2
%P 189--222
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%@ 0169-023X
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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.
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%U http://portal.acm.org/citation.cfm?id=606457
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