Doerfel, S.; Jäschke, R. & Stumme, G.: Publication Analysis of the Formal Concept Analysis Community. In: Domenach, F.; Ignatov, D. & Poelmans, J. (Hrsg.):
ICFCA 2012. Berlin/Heidelberg: Springer, 2012 (Lecture Notes in Artificial Intelligence 7278), S. 77-95
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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.: Discovering Shared Conceptualizations in Folksonomies. In:
Journal of Web Semantics 6 (2008), Nr. 1, S. 38-53
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Cimiano, P.; Hotho, A.; Stumme, G. & Tane, J.: Conceptual Knowledge Processing with Formal Concept Analysis and Ontologies. In: Eklund, P. (Hrsg.):
Concept Lattices. Berlin/Heidelberg: Springer, 2004 (Lecture Notes in Computer Science 2961), S. 189-207
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Stumme, G.: Iceberg Query Lattices for Datalog. In: Wolff, K. E.; Pfeiffer, H. D. & Delugach, H. S. (Hrsg.):
Conceptual Structures at Work: 12th International Conference on Conceptual Structures (ICCS 2004). Heidelberg: Springer, 2004 (LNCS 3127), S. 109-125
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[BibTeX]
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
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[Kurzfassung]
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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.