Doerfel, S.; Jäschke, R. & Stumme, G. (2012),
Publication Analysis of the Formal Concept Analysis Community, in
F. Domenach; D.I. Ignatov & J. Poelmans, ed.,
'ICFCA 2012'
, Springer, Berlin/Heidelberg
, pp. 77--95
.
[BibTeX]
[Endnote]
We present an analysis of the publication and citation networks of all previous editions of the three conferences most relevant to the FCA community: ICFCA, ICCS and CLA. Using data mining methods from FCA and graph analysis, we investigate patterns and communities among authors, we identify and visualize influential publications and authors, and we give a statistical summary of the conferences’ history.
Jäschke, R.; Hotho, A.; Schmitz, C.; Ganter, B. & Stumme, G. (2008),
'Discovering Shared Conceptualizations in Folksonomies', Journal of Web Semantics
6
(1)
, 38-53
.
[BibTeX]
[Endnote]
Cimiano, P.; Hotho, A.; Stumme, G. & Tane, J. (2004),
Conceptual Knowledge Processing with Formal Concept
Analysis and Ontologies, in Peter Eklund, ed.,
'Concept Lattices'
, Springer, Berlin/Heidelberg
, pp. 189--207
.
[BibTeX]
[Endnote]
Stumme, G. (2004),
Iceberg Query Lattices for Datalog, in
Karl Erich Wolff; Heather D. Pfeiffer & Harry S. Delugach, ed.,
'Conceptual Structures at Work: 12th International Conference on Conceptual Structures (ICCS 2004)'
, Springer, Heidelberg
, pp. 109-125
.
[BibTeX]
[Endnote]
Stumme, G.; Taouil, R.; Bastide, Y.; Pasquier, N. & Lakhal, L. (2002),
'Computing iceberg concept lattices with TITANIC', Data & Knowledge Engineering
42
(2)
, 189--222
.
[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.