%0 
%0 Conference Proceedings
%A Jäschke, Robert & Rudolph, Sebastian
%D 2013
%T Attribute Exploration on the Web
%E Cellier, Peggy; Distel, Felix & Ganter, Bernhard
%B Contributions to the 11th International Conference on Formal Concept Analysis
%C 
%I 
%V 
%6
%N 
%P 19--34
%& 
%Y Technische Universität Dresden
%S 
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%8 May
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%F jaschke2013attribute
%K 2013, acquisition, analysis, attribute, computing, concept, crowdsourcing, data, exploration, fca, formal, human, information, ir, iteg, knowledge, l3s, linked, lod, open, retrieval, search, sparql, web
%X We propose an approach for supporting attribute exploration by web information retrieval, in particular by posing appropriate queries to search engines, crowd sourcing systems, and the linked open data cloud. We discuss underlying general assumptions for this to work and the degree to which these can be taken for granted.
%Z 
%U http://nbn-resolving.de/urn:nbn:de:bsz:14-qucosa-113133
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%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
%N 
%P 77--95
%& 
%Y 
%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.

%Z 
%U https://www.kde.cs.uni-kassel.de/pub/pdf/doerfel2012publication.pdf
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%0 
%0 Book Section
%A Poelmans, Jonas; Ignatov, DmitryI.; Viaene, Stijn; Dedene, Guido & Kuznetsov, SergeiO.
%D 2012
%T Text Mining Scientific Papers: A Survey on FCA-Based Information Retrieval Research
%E Perner, Petra
%B Advances in Data Mining. Applications and Theoretical Aspects
%C 
%I Springer Berlin Heidelberg
%V 7377
%6
%N 
%P 273-287
%& 
%Y 
%S Lecture Notes in Computer Science
%7 
%8 
%9 
%? 
%! 
%Z 
%@ 978-3-642-31487-2
%(
%)
%*
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%1 
%2 Text Mining Scientific Papers: A Survey on FCA-Based Information Retrieval Research - Springer
%3 incollection
%4 
%# 
%$ 
%F noKey
%K FCA, IR, Mining, SOTA, Text
%X Formal Concept Analysis (FCA) is an unsupervised clustering technique and many scientific papers are devoted to applying FCA in Information Retrieval (IR) research. We collected 103 papers published between 2003-2009 which mention FCA and information retrieval in the abstract, title or keywords. Using a prototype of our FCA-based toolset CORDIET, we converted the pdf-files containing the papers to plain text, indexed them with Lucene using a thesaurus containing terms related to FCA research and then created the concept lattice shown in this paper. We visualized, analyzed and explored the literature with concept lattices and discovered multiple interesting research streams in IR of which we give an extensive overview. The core contributions of this paper are the innovative application of FCA to the text mining of scientific papers and the survey of the FCA-based IR research.
%Z 
%U http://dx.doi.org/10.1007/978-3-642-31488-9_22
%+
%^

%0 
%0 Conference Proceedings
%A Hotho, Andreas; J\"a,schke, Robert; Schmitz, Christoph & Stumme, Gerd
%D 2006
%T Information Retrieval in Folksonomies: Search and Ranking
%E 
%B Proceedings of the 3rd European Semantic Web Conference
%C 
%I Springer
%V 
%6
%N 
%P 411-426
%& 
%Y 
%S Lecture Notes in Computer Science
%7
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%F hotho2006information
%K FCA, OntologyHandbook, folkrank, folksonomy, graph, information, mining, pagerank, rank, ranking, retrieval, search, seminar2006
%X 
%Z 
%U 
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%^

%0 
%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
%& 
%Y 
%S Lecture Notes in Computer Science
%7 
%8 
%9 
%? 
%! 
%Z 
%@ 978-3-540-21043-6
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%)
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%3 incollection
%4 
%# 
%$ 
%F cimiano2004conceptual
%K 2004, citedBy:doerfel2012publication, fca, icfca, itegpub, knowledge, l3s, myown, processing
%X 
%Z 
%U http://dx.doi.org/10.1007/978-3-540-24651-0_18
%+
%^

%0 
%0 Conference Proceedings
%A Calders, Toon & Goethals, Bart
%D 2002
%T Mining All Non-derivable Frequent Itemsets
%E 
%B PKDD '02: Proceedings of the 6th European Conference on Principles
    of Data Mining and Knowledge Discovery
%C London, UK
%I Springer-Verlag
%V 
%6
%N 
%P 74--85
%& 
%Y 
%S 
%7
%8 
%9
%? 
%! 
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%@ 3-540-44037-2
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%3 inproceedings
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%F calders02mining
%K FCA, OntologyHandbook
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%U 
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%^

%0 
%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
%& 
%Y 
%S 
%7
%8 
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%@ 0169-023X
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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.
%Z 
%U http://portal.acm.org/citation.cfm?id=606457
%+
%^

%0 
%0 Conference Proceedings
%A Burdick, D.; Calimlim, M. & Gehrke, J.
%D 2001
%T MAFIA: A maximal frequent itemset algorithm for transactional databases
%E 
%B Proc. of the 17th Int. Conf. on Data Engineering
%C 
%I IEEE Computer Society
%V 
%6
%N 
%P 
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%F burdick01mafia
%K FCA, OntologyHandbook
%X 
%Z 
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%^

%0 
%0 Conference Proceedings
%A Bykowski, Artur & Rigotti, Christophe
%D 2001
%T A condensed representation to find frequent patterns.
%E 
%B PODS
%C 
%I 
%V 
%6
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%F Bykowski01condensed
%K FCA, OntologyHandbook, dblp
%X 
%Z 
%U http://dblp.uni-trier.de/db/conf/pods/pods2001.html#BykowskiR01
%+
%^

%0 
%0 Thesis
%A Bastide, Y.
%D 2000
%T Data Mining: algorithmes par niveau, techniques d'implementation
    et applications
%E 
%B 
%C 
%I Université de Clermont-Ferrand II
%V 
%6
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%K FCA, OntologyHandbook
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%^

%0 
%0 Conference Proceedings
%A Boulicaut, Jean-Francois; Bykowski, Artur & Rigotti, Christophe
%D 2000
%T Approximation of Frequency Queries by Means of Free-Sets
%E 
%B PKDD '00: Proceedings of the 4th European Conference on Principles
    of Data Mining and Knowledge Discovery
%C London, UK
%I Springer-Verlag
%V 
%6
%N 
%P 75--85
%& 
%Y 
%S 
%7
%8 
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%@ 3-540-41066-X
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%F boulicaut00approximation
%K FCA, OntologyHandbook
%X 
%Z 
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%0 
%0 Report
%A Burmeister, P.
%D 1998
%T Formal concept analysis with ConImp : Introduction to the
    basic features
%E 
%B 
%C Darmstadt, Germany
%I Technische Hochschule Darmstadt
%V 
%6
%N 
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%S 
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%3 techreport
%4 
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%F Burmeister98
%K FCA, OntologyHandbook
%X 
%Z http://www.mathematik.tu-darmstadt.de/~burmeister/ConImpIntro.ps
%U 
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%^

%0 
%0 Conference Proceedings
%A Fayet, A.; Giacometti, A.; Laurent, D. & Spyratos, N.
%D 1998
%T Decouverte de r�gles pertinentes dans les bases de donn�es
%E 
%B Actes des 14�mes journ�es Bases de Donn�es Avanc�es (BDA'98)
%C 
%I 
%V 
%6
%N 
%P 197--211
%& 
%Y 
%S 
%7
%8 Octobre
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%K FCA, OntologyHandbook
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%0 
%0 Conference Proceedings
%A Brin, S.; Motwani, R. & Silverstein, C.
%D 1997
%T Beyond market baskets: Generalizing association rules to correlation
%E 
%B Proceedings of the 1997 ACM SIGMOD international conference on Management
    of Data (SIGMOD'97)
%C 
%I ACM Press
%V 
%6
%N 
%P 265--276
%& 
%Y 
%S 
%7
%8 May
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%K FCA, OntologyHandbook
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%0 
%0 Conference Proceedings
%A Brin, S.; Motwani, R.; Ullman, J. D. & Tsur, S.
%D 1997
%T Dynamic itemset counting and implication rules for market basket
    data
%E 
%B Proceedings of the 1997 ACM SIGMOD international conference on Management
    of Data (SIGMOD'97)
%C 
%I ACM Press
%V 
%6
%N 
%P 255--264
%& 
%Y 
%S 
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%8 May
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%K FCA, OntologyHandbook
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%0 
%0 Book Section
%A Fayyad, U. M.; Piatetsky-Shapiro, G. & Smyth, P.
%D 1996
%T From data mining to knowledge discovery : An overview
%E Fayyad, U. M.; Piatetsky-Shapiro, G.; Smyth, P. & Uthurusamy, R.
%B Advances in Knowledge Discovery and Data Mining
%C 
%I AAAI Press
%V 
%6
%N 
%P 1--30
%& 
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%K FCA, OntologyHandbook
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%0 
%0 Conference Proceedings
%A Fayyad, U. M.; Piatetsky-Shapiro, G. & Smyth, P.
%D 1996
%T Knowledge discovery and data mining : Towards a unifying framework
%E 
%B Proceedings of the 2nd international conference on Knowledge Discovery
    and Data mining (KDD'96)
%C 
%I AAAI Press
%V 
%6
%N 
%P 82--88
%& 
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%S 
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%8 August
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%F Fayyad96b
%K FCA, OntologyHandbook
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%Z 
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%^

%0 
%0 Conference Proceedings
%A Feldman, R. & Dagan, I.
%D 1995
%T Knowledge discovery in textual databases
%E 
%B Proceedings of the 1st international conference on Knowledge Discovery
    and Data mining (KDD'95)
%C 
%I AAAI Press
%V 
%6
%N 
%P 112--117
%& 
%Y 
%S 
%7
%8 August
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%3 inproceedings
%4 
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%F Feldman95
%K FCA, OntologyHandbook
%X 
%Z 
%U 
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%^

%0 
%0 Book
%A Breiman, L.; Friedman, J. H.; Olshen, R. A. & Stone, C. J.
%D 1984
%T Classification and regression trees
%E 
%B 
%C 
%I Wadsworth Publishing Company
%V 
%6
%N 
%P
%& 
%Y 
%S 
%7 
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%3 book
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%F Breiman84
%K FCA, OntologyHandbook
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%Z 
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%0 
%0 Book
%A Brugger, W.
%D 1976
%T Philosophisches W�rterbuch
%E 
%B 
%C Freiburg
%I Herder
%V 
%6
%N 
%P
%& 
%Y 
%S 
%7 
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%3 book
%4 
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%F brugger76philosophisches
%K FCA, OntologyHandbook, Philosophie, philosophy
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