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}, issn = {0169-023X}, keywords = {titanic, itegpub, icfca, l3s, fca, myown, citedBy:doerfel2012publication}, 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.} }

%0 = article %A = Stumme, Gerd and Taouil, Rafik and Bastide, Yves and Pasquier, Nicolas and Lakhal, Lotfi %C = Amsterdam, The Netherlands, The Netherlands %D = 2002 %I = Elsevier Science Publishers B. V. %T = Computing iceberg concept lattices with TITANIC %U = http://portal.acm.org/citation.cfm?id=606457