%0 Journal Article %1 stumme2002computing %A Stumme, Gerd %A Taouil, Rafik %A Bastide, Yves %A Pasquier, Nicolas %A Lakhal, Lotfi %C Amsterdam, The Netherlands %D 2002 %I Elsevier Science Publishers B. V. %J Data & Knowledge Engineering %K %N 2 %P 189--222 %R 10.1016/S0169-023X(02)00057-5 %T Computing Iceberg Concept Lattices with Titanic %U http://www.kde.cs.uni-kassel.de/pub/pdf/stumme2002computing.pdf %V 42 %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.

@article{stumme2002computing, 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.}, address = {Amsterdam, The Netherlands}, author = {Stumme, Gerd and Taouil, Rafik and Bastide, Yves and Pasquier, Nicolas and Lakhal, Lotfi}, biburl = {https://www.bibsonomy.org/bibtex/227246f8ae440d6e8a5458fe50ffdde29/folke}, doi = {10.1016/S0169-023X(02)00057-5}, interhash = {d500ac8a249ca8bf0fb05f382799d48f}, intrahash = {27246f8ae440d6e8a5458fe50ffdde29}, issn = {0169-023X}, journal = {Data & Knowledge Engineering}, keywords = {}, month = aug, number = 2, pages = {189--222}, publisher = {Elsevier Science Publishers B. V.}, timestamp = {2011-09-22T11:56:28.000+0200}, title = {Computing Iceberg Concept Lattices with Titanic}, url = {http://www.kde.cs.uni-kassel.de/pub/pdf/stumme2002computing.pdf}, volume = 42, year = 2002 }