J 
Stumme, G.; Taouil, R.; Bastide, Y.; Pasquier, N. & Lakhal, L.
(2002):
Computing iceberg concept lattices with TITANIC.
In: Data & Knowledge Engineering,
Ausgabe/Number: 2,
Vol. 42,
Verlag/Publisher: Elsevier Science Publishers B. V..
Erscheinungsjahr/Year: 2002.
Seiten/Pages: 189222.
[Volltext] [Kurzfassung] [BibTeX]
[Endnote]
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 levelwise approach. In fact, TITANIC can be used for a more general problem: Computing arbitrary closure systems when the closure operator comes along with a socalled 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 NextClosure 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 = {189222},
url = {http://portal.acm.org/citation.cfm?id=606457},
doi = {10.1016/S0169023X(02)000575},
issn = {0169023X},
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 levelwise approach. In fact, TITANIC can be used for a more general problem: Computing arbitrary closure systems when the closure operator comes along with a socalled 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 NextClosure 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
