%0 
%0 Conference Proceedings
%A Doerfel, Stephan; Jäschke, Robert & Stumme, Gerd
%D 2012
%T Publication Analysis of the Formal Concept Analysis Community
%E Domenach, F.; Ignatov, D.I. & Poelmans, J.
%B ICFCA 2012
%C Berlin/Heidelberg
%I Springer
%V 7278
%6
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%P 77--95
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%S Lecture Notes in Artificial Intelligence
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%F doerfel2012publication
%K 2012, analysis, community, concept, fca, home, info20pub, itegpub, l3s, myown, scientometrics
%X 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.

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%U https://www.kde.cs.uni-kassel.de/pub/pdf/doerfel2012publication.pdf
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%0 Journal Article
%A Jäschke, Robert; Hotho, Andreas; Schmitz, Christoph; Ganter, Bernhard & Stumme, Gerd
%D 2008
%T Discovering Shared Conceptualizations in Folksonomies
%E 
%B Journal of Web Semantics
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%I 
%V 6
%6
%N 1
%P 38-53
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%F jaeschke08discovering
%K 2008, FCA, OntologyHandbook, analysis, bibsonomy, concept, discovering, fca, folksonomies, formal, itegpub, l3s, myown, shared, triadic
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%U http://dx.doi.org/10.1016/j.websem.2007.11.004
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%0 Book Section
%A Cimiano, Philipp; Hotho, Andreas; Stumme, Gerd & Tane, Julien
%D 2004
%T Conceptual Knowledge Processing with Formal Concept
                   Analysis and Ontologies
%E Eklund, Peter
%B Concept Lattices
%C Berlin/Heidelberg
%I Springer
%V 2961
%6
%N 
%P 189--207
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%Y 
%S Lecture Notes in Computer Science
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%@ 978-3-540-21043-6
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%F cimiano2004conceptual
%K 2004, citedBy:doerfel2012publication, fca, icfca, itegpub, knowledge, l3s, myown, processing
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%U http://dx.doi.org/10.1007/978-3-540-24651-0_18
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%0 Conference Proceedings
%A Stumme, Gerd
%D 2004
%T Iceberg Query Lattices for Datalog
%E Wolff, Karl Erich; Pfeiffer, Heather D. & Delugach, Harry S.
%B Conceptual Structures at Work: 12th International Conference on Conceptual Structures (ICCS 2004)
%C Heidelberg
%I Springer
%V 3127
%6
%N 
%P 109-125
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%S LNCS
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%F stumme2004iceberg
%K 2004, analysis, concept, context, datalog, families, family, fca, formal, iceberg, itegpub, l3s, lattices, myown, pcf, power, queries, query
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%U http://www.kde.cs.uni-kassel.de/stumme/papers/2004/stumme2004iceberg.pdf
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%0 Journal Article
%A Stumme, Gerd; Taouil, Rafik; Bastide, Yves; Pasquier, Nicolas & Lakhal, Lotfi
%D 2002
%T Computing iceberg concept lattices with TITANIC
%E 
%B Data \& Knowledge Engineering
%C 
%I Elsevier Science Publishers B. V.
%V 42
%6
%N 2
%P 189--222
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%F stumme2002computing
%K citedBy:doerfel2012publication, fca, icfca, itegpub, l3s, myown, titanic
%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.
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%U http://portal.acm.org/citation.cfm?id=606457
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