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: 189-222.
[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 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.

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