Pasquier, N.; Taouil, R.; Bastide, Y.; Stumme, G. & Lakhal, L.: Generating a Condensed Representation for Association Rules. In: Journal Intelligent Information Systems (JIIS) 24 (2005), Nr. 1, S. 29-60
[Volltext]
@article{pasquier2005generating,
author = {Pasquier, Nicolas and Taouil, Rafik and Bastide, Yves and Stumme, Gerd and Lakhal, Lotfi},
title = {Generating a Condensed Representation for Association Rules},
journal = {Journal Intelligent Information Systems (JIIS)},
publisher = {Kluwer Academic Publishers},
year = {2005},
volume = {24},
number = {1},
pages = {29-60},
url = {http://www.kde.cs.uni-kassel.de/stumme/papers/2005/pasquier2005generating.pdf},
keywords = {concept, discovery, 2005, association, OntologyHandbook, l3s, analysis, kdd, itemset, myown, rule, data, knowledge, closed, formal, rules, itegpub, condensed, fca, sets, representations, mining}
}
Pasquier, N.; Taouil, R.; Bastide, Y.; Stumme, G. & Lakhal, L.: Generating a Condensed Representation for Association Rules. In: Journal Intelligent Information Systems (JIIS) 24 (2005), Nr. 1, S. 29-60
[Volltext]
@article{pasquier2005generating,
author = {Pasquier, Nicolas and Taouil, Rafik and Bastide, Yves and Stumme, Gerd and Lakhal, Lotfi},
title = {Generating a Condensed Representation for Association Rules},
journal = {Journal Intelligent Information Systems (JIIS)},
publisher = {Kluwer Academic Publishers},
year = {2005},
volume = {24},
number = {1},
pages = {29-60},
url = {http://www.kde.cs.uni-kassel.de/stumme/papers/2005/pasquier2005generating.pdf},
keywords = {concept, discovery, 2005, association, OntologyHandbook, l3s, analysis, kdd, itemset, myown, rule, data, knowledge, closed, formal, rules, itegpub, condensed, fca, sets, representations, mining}
}
Stumme, G.; Taouil, R.; Bastide, Y.; Pasquier, N. & Lakhal, L.: Computing iceberg concept lattices with TITANIC. In: Data & Knowledge Engineering 42 (2002), Nr. 2, S. 189-222
[Volltext]
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},
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.}
}