%0 
%0 Journal Article
%A Keim, D. & Kriegel, H.
%D 1996
%T Visualization techniques for mining large databases : A comparison
%E 
%B IEEE Transactions on Knowledge and Data Engineering
%C 
%I IEEE Computer Society Press
%V 8
%6
%N 6
%P 923--938
%& 
%Y 
%S 
%7
%8 December
%9
%? 
%! 
%Z 
%@ 
%(
%)
%*
%L
%M
%1 
%2 fca
%3 article
%4 
%# 
%$ 
%F Keim96
%K FCA, OntologyHandbook
%X 
%Z 
%U 
%+
%^

%0 
%0 Book Section
%A Stumme, G.
%D 2002
%T Using Ontologies and Formal Concept Analysis for Organizing Business Knowledge
%E Becker, J. & Knackstedt, R.
%B Wissensmanagement mit Referenzmodellen -- Konzepte für die Anwendungssystem- und Organisationsgestaltung
%C Heidelberg
%I Physica
%V 
%6
%N 
%P 163-174
%& 
%Y 
%S 
%7 
%8 
%9 
%? 
%! 
%Z 
%@ 
%(
%)
%*
%L
%M
%1 
%2 Publications of Gerd Stumme
%3 incollection
%4 
%# 
%$ 
%F stumme02using
%K 2002, analysis, business, concept, fca, formal, knowledge, management, myown, ontologies, semantic, web, wissensmanagement
%X 
%Z 
%U http://www.kde.cs.uni-kassel.de/stumme/papers/2001/REFMOD01.ps
%+
%^

%0 
%0 Book
%A Habermas, J�rgen
%D 1981
%T Theorie des kommunikativen Handelns
%E 
%B 
%C Frankfurt
%I Suhrkamp
%V 
%6
%N 
%P
%& 
%Y 
%S 
%7 
%8 
%9
%? 
%! 
%Z 
%@ 
%(
%)
%*
%L
%M
%1 
%2 fca
%3 book
%4 
%# 
%$ 
%F habermas81theorie
%K FCA, OntologyHandbook, fca, habermas, handeln, handelns, kommunikativen, kommunikatives, theorie
%X 
%Z 
%U 
%+
%^

%0 
%0 Report
%A Golder, Scott & Huberman, Bernardo A.
%D 2005
%T The Structure of Collaborative Tagging Systems
%E 
%B 
%C 
%I Information Dynamics Lab, HP Labs    
%V 
%6
%N 
%P 
%& 
%Y 
%S 
%7
%8 August
%9 
%? 
%! 
%Z 
%@ 
%(
%)
%*
%L
%M
%1 
%2 fca
%3 techreport
%4 
%# 
%$ 
%F GH05structure
%K FCA, OntologyHandbook, folksonomy, structure, tagging
%X 
%Z 
%U http://arxiv.org/abs/cs.DL/0508082
%+
%^

%0 
%0 Conference Proceedings
%A Tane, Julien; Schmitz, Christoph; Stumme, Gerd; Staab, Steffen & Studer, R.
%D 2003
%T The Courseware Watchdog:  an Ontology-based tool for finding and organizing
                  learning material
%E David, Klaus & Wegner, Lutz
%B Mobiles Lernen und Forschen - Beiträge der Fachtagung an der Universität
%C 
%I Kassel University Press
%V 
%6
%N 
%P 93-104
%& 
%Y 
%S 
%7
%8 November
%9
%? 
%! 
%Z 
%@ 
%(
%)
%*
%L
%M
%1 
%2 Publications of Gerd Stumme
%3 inproceedings
%4 
%# 
%$ 
%F tane03courseware
%K 2003, analysis, concept, courseware, crawling, e-learning, eLearning, edutella, fca, formal, myown, ontologies, ontology, p2p, padlr, watchdog
%X Topics in education are changing with an ever faster pace. E-Learning
resources tend to be more and more decentralised. Users need increasingly to be able to
use the resources of the web. For this, they should have tools for finding and organizing
information in a decentral way. In this, paper, we show how an ontology-based tool
suite allows to make the most of the resources available on the web.
%Z 
%U http://www.kde.cs.uni-kassel.de/stumme/papers/2003/tane2003courseware.pdf
%+
%^

%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
%(
%)
%*
%L
%M
%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 Report
%A Hotho, Andreas; Staab, Steffen & Stumme, Gerd
%D 2003
%T Text Clustering Based on Background Knowledge
%E 
%B 
%C 
%I University of Karlsruhe, Institute AIFB
%V 
%6
%N 
%P 
%& 
%Y 
%S 
%7
%8 
%9 Technical Report 
%? 
%! 
%Z 
%@ 
%(
%)
%*
%L
%M
%1 
%2 Publications of Gerd Stumme
%3 techreport
%4 
%# 
%$ 
%F hotho03textclustering
%K 2003, analysis, background, clustering, concept, fca, formal, knowledge, myown, ontologies, semantic, text, web
%X Text document clustering plays an important role in providing intuitive
navigation and browsing mechanisms by organizing large amounts of information
into a small number of meaningful clusters. Standard partitional or agglomerative
clustering methods efficiently compute results to this end.
However, the bag of words representation used for these clustering methods is often
unsatisfactory as it ignores relationships between important terms that do not
co-occur literally. Also, it is mostly left to the user to find out why a particular partitioning
has been achieved, because it is only specified extensionally. In order to
deal with the two problems, we integrate background knowledge into the process of
clustering text documents.
First, we preprocess the texts, enriching their representations by background knowledge
provided in a core ontology — in our application Wordnet. Then, we cluster
the documents by a partitional algorithm. Our experimental evaluation on Reuters
newsfeeds compares clustering results with pre-categorizations of news. In the experiments,
improvements of results by background knowledge compared to the baseline
can be shown for many interesting tasks.
Second, the clustering partitions the large number of documents to a relatively small
number of clusters, which may then be analyzed by conceptual clustering. In our approach,
we applied Formal Concept Analysis. Conceptual clustering techniques are
known to be too slow for directly clustering several hundreds of documents, but they
give an intensional account of cluster results. They allow for a concise description
of commonalities and distinctions of different clusters. With background knowledge
they even find abstractions like “food” (vs. specializations like “beef” or “corn”).
Thus, in our approach, partitional clustering reduces first the size of the problem
such that it becomes tractable for conceptual clustering, which then facilitates the
understanding of the results.
%Z 
%U http://www.kde.cs.uni-kassel.de/stumme/papers/2003/hotho2003text.pdf
%+
%^

%0 
%0 Conference Proceedings
%A Houtsma, M. & Swami, A.
%D 1995
%T Set-oriented mining for association rules in relational databases
%E 
%B Proceedings of the 11th International Conference on Data Engineering
    (ICDE'95)
%C 
%I IEEE Computer Society Press
%V 
%6
%N 
%P 25--33
%& 
%Y 
%S 
%7
%8 March
%9
%? 
%! 
%Z 
%@ 
%(
%)
%*
%L
%M
%1 
%2 fca
%3 inproceedings
%4 
%# 
%$ 
%F Houtsma95
%K FCA, OntologyHandbook
%X 
%Z 
%U 
%+
%^

%0 
%0 Conference Proceedings
%A Tane, Julien; Schmitz, Christoph & Stumme, Gerd
%D 2004
%T Semantic resource management for the web: an e-learning application
%E 
%B Proc. 13th International World Wide Web Conference (WWW 2004)
%C 
%I 
%V 
%6
%N 
%P 1-10
%& 
%Y 
%S 
%7
%8 
%9
%? 
%! 
%Z 
%@ 
%(
%)
%*
%L
%M
%1 
%2 Publications of Gerd Stumme
%3 inproceedings
%4 
%# 
%$ 
%F tane04semantic
%K 2004, application, courseware, crawler, e-learning, eLearning, edutella, fca, itegpub, l3s, learning, myown, p2p, semantic, watchdog, web
%X 
%Z 
%U http://www.www2004.org/proceedings/docs/2p1.pdf
%+
%^

%0 
%0 Conference Proceedings
%A Hereth, J. & Stumme, G.
%D 2001
%T Reverse Pivoting in Conceptual Information Systems.
%E Delugach, H. & Stumme, G.
%B Conceptual Structures: Broadening the Base. 
%C Heidelberg
%I Springer
%V 2120
%6
%N 
%P 202-215
%& 
%Y 
%S LNAI
%7
%8 
%9
%? 
%! 
%Z 
%@ 
%(
%)
%*
%L
%M
%1 
%2 Publications of Gerd Stumme
%3 inproceedings
%4 
%# 
%$ 
%F hereth01reverse
%K 2001, analysis, concept, conceptual, fca, formal, information, myown, pivoting, reverse, systems, toscana
%X 
%Z 
%U http://www.kde.cs.uni-kassel.de/stumme/papers/2001/ICCS01.pdf
%+
%^

%0 
%0 Conference Proceedings
%A Hereth, J. & Stumme, G.
%D 2001
%T Reverse Pivoting in Conceptual Information Systems
%E Delugach, H. S. & Stumme, G.
%B Conceptual Structures: Broadening the Base
%C 
%I Springer
%V 2120
%6
%N 
%P 202-215
%& 
%Y 
%S Lecture Notes in Computer Science
%7
%8 
%9
%? 
%! 
%Z 
%@ 3-540-42344-3
%(
%)
%*
%L
%M
%1 
%2 fca
%3 inproceedings
%4 
%# 
%$ 
%F HerethStumme01Reverse
%K FCA, OntologyHandbook
%X 
%Z 
%U 
%+
%^

%0 
%0 Conference Proceedings
%A Hereth, J.
%D 2002
%T Relational Scaling and Databases
%E Priss, U.; Corbett, D. & Angelova, G.
%B Conceptual Structures: Integration and Interfaces, 10th International
    Conference on Conceptual Structures, ICCS 2002, Borovets, Bulgaria,
    July 15-19, 2002, Proceedings
%C 
%I Springer
%V 2393
%6
%N 
%P 62-76
%& 
%Y 
%S Lecture Notes in Computer Science
%7
%8 
%9
%? 
%! 
%Z 
%@ 3-540-43901-3
%(
%)
%*
%L
%M
%1 
%2 fca
%3 inproceedings
%4 
%# 
%$ 
%F Hereth02Relational
%K FCA, OntologyHandbook
%X 
%Z 
%U 
%+
%^

%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
%7
%8 
%9
%? 
%! 
%Z 
%@ 
%(
%)
%*
%L
%M
%1 
%2 
%3 inproceedings
%4 
%# 
%$ 
%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
%+
%^

%0 
%0 Conference Proceedings
%A Wille, Rudolf
%D 1994
%T Plädoyer für eine philosophische Grundlegung der Begrifflichen Wissensverarbeitung
%E Wille, R. & Zickwolff, M.
%B Begriffliche Wissensverarbeitung -- Grundfragen und Aufgaben
%C Mannheim
%I B. I. -Wissenschaftsverlag
%V 
%6
%N 
%P 11-25
%& 
%Y 
%S 
%7
%8 
%9
%? 
%! 
%Z 
%@ 
%(
%)
%*
%L
%M
%1 
%2 fca
%3 inproceedings
%4 
%# 
%$ 
%F wille94plaedoyer
%K FCA, OntologyHandbook, analysis, begriffliche, concept, conceptual, fca, formal, knowledge, processing, wissensverarbeitung
%X 
%Z 
%U 
%+
%^

%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 
%8 
%9
%? 
%! 
%Z 
%@ 
%(
%)
%*
%L
%M
%1 
%2 fca
%3 book
%4 
%# 
%$ 
%F brugger76philosophisches
%K FCA, OntologyHandbook, Philosophie, philosophy
%X 
%Z 
%U 
%+
%^

%0 
%0 Conference Proceedings
%A Stumme, Gerd
%D 1998
%T On-Line Analytical Processing with Conceptual Information Systems
%E Tanaka, K. & Ghandeharizadeh, S.
%B Proc. 5th Intl. Conf. on Foundations of Data Organization (FODO'98)
%C 
%I 
%V 
%6
%N 
%P 117-126
%& 
%Y 
%S 
%7
%8 nov 12-13
%9
%? 
%! 
%Z 
%@ 
%(
%)
%*
%L
%M
%1 
%2 
%3 inproceedings
%4 
%# 
%$ 
%F stumme98online
%K 1998, OntologyHandbook, analysis, analytical, concept, conceptual, fca, formal, myown, olap, online, processing
%X 
%Z Short version of \cite{stumme00conceptual}
%U http://www.kde.cs.uni-kassel.de/stumme/papers/1998/FODO98.pdf
%+
%^

%0 
%0 Conference Proceedings
%A Goethals, Bart; Muhonen, Juhu & Toivonen, Hannu
%D 2005
%T Mining non-derivable association rules
%E 
%B Proc. SIAM International Conference on Data Mining
%C Newport Beach, CA
%I 
%V 
%6
%N 
%P 
%& 
%Y 
%S 
%7
%8 April
%9
%? 
%! 
%Z 
%@ 
%(
%)
%*
%L
%M
%1 
%2 fca
%3 inproceedings
%4 
%# 
%$ 
%F goethals05mining
%K FCA, OntologyHandbook
%X 
%Z 
%U 
%+
%^

%0 
%0 Manuscript
%A Koperski, K.; Han, J. & Adhikary, J.
%D 1998
%T Mining knowledge in geographical data
%E
%B 
%C
%I
%V 
%6
%N 
%P 
%& 
%Y 
%S 
%7
%8 
%9
%? 
%! 
%Z 
%@ 
%(
%)
%*
%L
%M
%1 
%2 fca
%3 unpublished
%4 
%# 
%$ 
%F Koperski98
%K FCA, OntologyHandbook
%X 
%Z Technical Report. ftp://ftp.fas.sfu.ca/pub/cs/han/ kdd/geo\_survey98.ps
%U 
%+
%^

%0 
%0 Conference Proceedings
%A Han, J.; Pei, J. & Yin, Y.
%D 2000
%T Mining frequent patterns without candidate generation.
%E 
%B Proc. ACM SIGMOD Int'l Conf. on Management of Data
%C 
%I 
%V 
%6
%N 
%P 1--12
%& 
%Y 
%S 
%7
%8 May
%9
%? 
%! 
%Z 
%@ 
%(
%)
%*
%L
%M
%1 
%2 fca
%3 inproceedings
%4 
%# 
%$ 
%F Han00mining
%K FCA, OntologyHandbook
%X 
%Z 
%U 
%+
%^

%0 
%0 Conference Proceedings
%A Schmitz, Christoph; Hotho, Andreas; Jäschke, Robert & Stumme, Gerd
%D 2006
%T Mining Association Rules in Folksonomies
%E Batagelj, V.; Bock, H.-H.; Ferligoj, A. & Žiberna, A.
%B Data Science and Classification. Proceedings of the 10th IFCS Conf.
%C Heidelberg
%I Springer
%V 
%6
%N 
%P 261--270
%& 
%Y 
%S Studies in Classification, Data Analysis, and Knowledge Organization
%7
%8 July
%9
%? 
%! 
%Z 
%@ 
%(
%)
%*
%L
%M
%1 
%2 Publications of Gerd Stumme
%3 inproceedings
%4 
%# 
%$ 
%F schmitz2006mining
%K 2006, analysis, fca, folksonomies, folksonomy, l3s, myown, nepomuk, network, semantic
%X Social bookmark tools are rapidly emerging on the Web. In such
systems users are setting up lightweight conceptual structures
called folksonomies. These systems provide currently relatively few
structure. We discuss in this paper, how association rule mining
can be adopted to analyze and structure folksonomies, and how the results can be used
for ontology learning and supporting emergent semantics. We
demonstrate our approach on a large scale dataset stemming from an
online system.
%Z 
%U http://www.kde.cs.uni-kassel.de/stumme/papers/2006/schmitz2006mining.pdf
%+
%^

%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
%? 
%! 
%Z 
%@ 3-540-44037-2
%(
%)
%*
%L
%M
%1 
%2 fca
%3 inproceedings
%4 
%# 
%$ 
%F calders02mining
%K FCA, OntologyHandbook
%X 
%Z 
%U 
%+
%^

%0 
%0 Conference Proceedings
%A Kamber, M.; Han, J. & Chiang, Y.
%D 1997
%T Metarule-guided mining of multi-dimensional association rules using
    data cubes.
%E 
%B Proc. of the 3rd KDD Int'l Conf.
%C 
%I 
%V 
%6
%N 
%P 
%& 
%Y 
%S 
%7
%8 August
%9
%? 
%! 
%Z 
%@ 
%(
%)
%*
%L
%M
%1 
%2 fca
%3 inproceedings
%4 
%# 
%$ 
%F Kamber97metarule
%K FCA, OntologyHandbook
%X 
%Z 
%U 
%+
%^

%0 
%0 Book
%A von Hentig, Hartmut
%D 1974
%T Magier oder Magister? �ber die Einheit der Wissenschaft im Verst�ndigungsprozess
%E 
%B 
%C Frankfurt
%I Suhrkamp
%V 
%6
%N 
%P
%& 
%Y 
%S 
%7 
%8 
%9
%? 
%! 
%Z 
%@ 
%(
%)
%*
%L
%M
%1 
%2 fca
%3 book
%4 
%# 
%$ 
%F hentig74magier
%K FCA, OntologyHandbook, fca, magier, magister, wissenscharft
%X 
%Z 
%U 
%+
%^

%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 
%& 
%Y 
%S 
%7
%8 
%9
%? 
%! 
%Z 
%@ 
%(
%)
%*
%L
%M
%1 
%2 fca
%3 inproceedings
%4 
%# 
%$ 
%F burdick01mafia
%K FCA, OntologyHandbook
%X 
%Z 
%U 
%+
%^

%0 
%0 Conference Proceedings
%A Groh, B. & Wille, R.
%D 2000
%T Lattices of Triadic Concept Graphs
%E Ganter, B. & Mineau, G. W.
%B Conceptual Structures: Logical, Linguistic, and Computational Issues
%C 
%I Springer
%V 1867
%6
%N 
%P 332-341
%& 
%Y 
%S Lecture Notes in Computer Science
%7
%8 
%9
%? 
%! 
%Z 
%@ 3-540-67859-X
%(
%)
%*
%L
%M
%1 
%2 fca
%3 inproceedings
%4 
%# 
%$ 
%F GrohWille00Lattices
%K FCA, OntologyHandbook
%X 
%Z 
%U 
%+
%^

%0 
%0 Book
%A Lamport, Leslie
%D 1986
%T LaTeX: A Document Preparation System
%E 
%B 
%C 
%I Addison-Wesley
%V 
%6
%N 
%P
%& 
%Y 
%S 
%7 
%8 
%9
%? 
%! 
%Z 
%@ 
%(
%)
%*
%L
%M
%1 
%2 fca
%3 book
%4 
%# 
%$ 
%F lamport86latex
%K FCA, OntologyHandbook, document, lamport, latex, system, typesetting
%X 
%Z 
%U 
%+
%^

%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
%9
%? 
%! 
%Z 
%@ 
%(
%)
%*
%L
%M
%1 
%2 fca
%3 inproceedings
%4 
%# 
%$ 
%F Feldman95
%K FCA, OntologyHandbook
%X 
%Z 
%U 
%+
%^

%0 
%0 Conference Proceedings
%A Han, J.; Cai, Y. & Cercone, N.
%D 1992
%T Knowledge discovery in databases : An attribute oriented approach
%E 
%B Proceedings of the 18th international conference on Very Large Data
    Bases (VLDB'92)
%C 
%I Morgan Kaufmann
%V 
%6
%N 
%P 547--559
%& 
%Y 
%S 
%7
%8 August
%9
%? 
%! 
%Z 
%@ 
%(
%)
%*
%L
%M
%1 
%2 fca
%3 inproceedings
%4 
%# 
%$ 
%F Han92
%K FCA, OntologyHandbook
%X 
%Z 
%U 
%+
%^

%0 
%0 Conference Proceedings
%A H�t�nen, K.; Klemettinen, M.; Mannila, H.; Ronkainen, P. & Toivonen, H.
%D 1996
%T Knowledge discovery from telecommunication network alarm databases
%E 
%B Proceedings of the 12th International Conference on Data Engineering
    (ICDE'96)
%C 
%I IEEE Computer Society Press
%V 
%6
%N 
%P 115--122
%& 
%Y 
%S 
%7
%8 February
%9
%? 
%! 
%Z 
%@ 
%(
%)
%*
%L
%M
%1 
%2 fca
%3 inproceedings
%4 
%# 
%$ 
%F Haetoenen96
%K FCA, OntologyHandbook
%X 
%Z 
%U 
%+
%^

%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
%& 
%Y 
%S 
%7
%8 August
%9
%? 
%! 
%Z 
%@ 
%(
%)
%*
%L
%M
%1 
%2 fca
%3 inproceedings
%4 
%# 
%$ 
%F Fayyad96b
%K FCA, OntologyHandbook
%X 
%Z 
%U 
%+
%^

%0 
%0 Journal Article
%A Fisher, D. H.
%D 1987
%T Knowledge acquisition via incremental conceptual clustering
%E 
%B Machine Learning
%C 
%I Kluwer Academic Publishers
%V 2
%6
%N 2
%P 139--172
%& 
%Y 
%S 
%7
%8 
%9
%? 
%! 
%Z 
%@ 
%(
%)
%*
%L
%M
%1 
%2 fca
%3 article
%4 
%# 
%$ 
%F Fisher87
%K FCA, OntologyHandbook
%X 
%Z 
%U 
%+
%^

%0 
%0 Generic
%A German Federal Office for Information Security,
%D 2003
%T IT Baseline Protection Manual
%E
%B 
%C
%I http://www.bsi.de/gshb/
%V 
%6
%N 
%P 
%& 
%Y 
%S 
%7
%8 October
%9
%? 
%! 
%Z 
%@ 
%(
%)
%*
%L
%M
%1 
%2 fca
%3 misc
%4 
%# 
%$ 
%F BSI03ITBaseline
%K FCA, OntologyHandbook
%X 
%Z 
%U 
%+
%^

%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
%8 
%9
%? 
%! 
%Z 
%@ 
%(
%)
%*
%L
%M
%1 
%2 
%3 inproceedings
%4 
%# 
%$ 
%F hotho2006information
%K FCA, OntologyHandbook, folkrank, folksonomy, graph, information, mining, pagerank, rank, ranking, retrieval, search, seminar2006
%X 
%Z 
%U 
%+
%^

%0 
%0 Journal Article
%A Godin, R.; Missaoui, R. & Alaoui, H.
%D 1995
%T Incremental concept formation algorithms based on Galois
    (concept) lattices
%E 
%B Computational Intelligence
%C 
%I Blackwell Publishers
%V 11
%6
%N 2
%P 246-267
%& 
%Y 
%S 
%7
%8 May
%9
%? 
%! 
%Z 
%@ 
%(
%)
%*
%L
%M
%1 
%2 fca
%3 article
%4 
%# 
%$ 
%F Godin95
%K FCA, OntologyHandbook
%X 
%Z 
%U 
%+
%^

%0 
%0 Conference Proceedings
%A Harinarayan, V.; Rajaraman, A. & Ullman, J. D.
%D 1996
%T Implementing data cubes efficiently
%E 
%B Proceedings of the 1996 ACM SIGMOD international conference on Management
    of Data (SIGMOD'96)
%C 
%I ACM Press
%V 
%6
%N 
%P 205--216
%& 
%Y 
%S 
%7
%8 June
%9
%? 
%! 
%Z 
%@ 
%(
%)
%*
%L
%M
%1 
%2 fca
%3 inproceedings
%4 
%# 
%$ 
%F Harinarayan96
%K FCA, OntologyHandbook
%X 
%Z 
%U 
%+
%^

%0 
%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
%& 
%Y 
%S LNCS
%7
%8 
%9
%? 
%! 
%Z 
%@ 
%(
%)
%*
%L
%M
%1 
%2 Publications of Gerd Stumme
%3 inproceedings
%4 
%# 
%$ 
%F stumme2004iceberg
%K 2004, analysis, concept, context, datalog, families, family, fca, formal, iceberg, itegpub, l3s, lattices, myown, pcf, power, queries, query
%X 
%Z 
%U http://www.kde.cs.uni-kassel.de/stumme/papers/2004/stumme2004iceberg.pdf
%+
%^

%0 
%0 Conference Proceedings
%A Han, J.; Koperski, K. & Stefanovic, N.
%D 1997
%T GeoMiner : A system prototype for spatial data mining
%E 
%B Proceedings of the 1997 ACM SIGMOD international conference on Management
    of Data (SIGMOD'97)
%C 
%I ACM Press
%V 
%6
%N 
%P 553--556
%& 
%Y 
%S 
%7
%8 May
%9
%? 
%! 
%Z 
%@ 
%(
%)
%*
%L
%M
%1 
%2 fca
%3 inproceedings
%4 
%# 
%$ 
%F Han97
%K FCA, OntologyHandbook
%X 
%Z 
%U 
%+
%^

%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
%& 
%Y 
%S 
%7 
%8 
%9 
%? 
%! 
%Z 
%@ 
%(
%)
%*
%L
%M
%1 
%2 fca
%3 incollection
%4 
%# 
%$ 
%F Fayyad96c
%K FCA, OntologyHandbook
%X 
%Z 
%U 
%+
%^

%0 
%0 Book
%A Ganter, B. & Wille, R.
%D 1999
%T Formal Concept Analysis: Mathematical foundations
%E 
%B 
%C 
%I Springer
%V 
%6
%N 
%P
%& 
%Y 
%S 
%7 
%8 
%9
%? 
%! 
%Z 
%@ 
%(
%)
%*
%L
%M
%1 
%2 fca
%3 book
%4 
%# 
%$ 
%F Ganter99
%K FCA, OntologyHandbook
%X 
%Z 
%U 
%+
%^

%0 
%0 Book
%A Ganter, B. & Wille, R.
%D 1999
%T Formal Concept Analysis: Mathematical Foundations
%E 
%B 
%C 
%I Springer
%V 
%6
%N 
%P
%& 
%Y 
%S 
%7 
%8 
%9
%? 
%! 
%Z 
%@ 
%(
%)
%*
%L
%M
%1 
%2 fca
%3 book
%4 
%# 
%$ 
%F ganter99formal
%K FCA, OntologyHandbook, analysis, concept, fca, formal, math
%X 
%Z Translation of: {\it Formale Be\-griffs\-ana\-ly\-se: Mathematische
    Grundlagen.} Sprin\-ger, Heidelberg 1996.
%U 
%+
%^

%0 
%0 Conference Proceedings
%A 
%D 2005
%T Formal Concept Analysis: Foundations and Applications
%E Ganter, B.; Stumme, G. & Wille, R.
%B 
%C Heidelberg
%I Springer
%V 3626
%6
%N 
%P 
%& 
%Y 
%S LNAI
%7
%8 
%9
%? 
%! 
%Z 
%@ 
%(
%)
%*
%L
%M
%1 
%2 Publications of Gerd Stumme
%3 proceedings
%4 
%# 
%$ 
%F ganter05formal
%K 2005, book, fca, l3s, myown
%X 
%Z http://www.informatik.uni-trier.de/~ley/db/conf/fca/fca2005.html
%U http://www.springerlink.com/content/978-3-540-27891-7/
%+
%^

%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 
%P 
%& 
%Y 
%S 
%7
%8 
%9 
%? 
%! 
%Z 
%@ 
%(
%)
%*
%L
%M
%1 
%2 fca
%3 techreport
%4 
%# 
%$ 
%F Burmeister98
%K FCA, OntologyHandbook
%X 
%Z http://www.mathematik.tu-darmstadt.de/~burmeister/ConImpIntro.ps
%U 
%+
%^

%0 
%0 Conference Proceedings
%A Stumme, G.
%D 2002
%T Formal Concept Analysis on its Way from Mathematics to Computer Science
%E Priss, U.; Corbett, D. & Angelova, G.
%B Conceptual Structures: Integration and Interfaces 
%C Heidelberg
%I Springer
%V 2393
%6
%N 
%P 2-19
%& 
%Y 
%S LNAI
%7
%8 
%9
%? 
%! 
%Z 
%@ 
%(
%)
%*
%L
%M
%1 
%2 alpha
%3 inproceedings
%4 
%# 
%$ 
%F stumme02formalconcept
%K 2002, OntologyHandbook, analysis, computer, concept, fca, formal, informatik, mathematics, mathematik, myown, science
%X 
%Z Invited Talk, summary of \cite{stumme03offtonew}
%U http://www.kde.cs.uni-kassel.de/stumme/papers/2002/ICCS02.pdf
%+
%^

%0 
%0 Conference Proceedings
%A Klemettinen, M.; Mannila, H.; Ronkainen, P.; Toivonen, H. & Verkamo, A. I.
%D 1994
%T Finding interesting rules from large sets of discovered association
    rules
%E 
%B Proceedings of the 3rd international Conference on Information and
    Knowledge Management (CIKM'94)
%C 
%I ACM Press
%V 
%6
%N 
%P 401--407
%& 
%Y 
%S 
%7
%8 November
%9
%? 
%! 
%Z 
%@ 
%(
%)
%*
%L
%M
%1 
%2 fca
%3 inproceedings
%4 
%# 
%$ 
%F Klemettinen94
%K FCA, OntologyHandbook
%X 
%Z 
%U 
%+
%^

%0 
%0 Journal Article
%A Ganter, B. & Reuter, K.
%D 1991
%T Finding all closed sets : A general approach
%E 
%B Order
%C 
%I Kluwer Academic Publishers
%V 8
%6
%N 
%P 283-290
%& 
%Y 
%S 
%7
%8 
%9
%? 
%! 
%Z 
%@ 
%(
%)
%*
%L
%M
%1 
%2 fca
%3 article
%4 
%# 
%$ 
%F Ganter91
%K FCA, OntologyHandbook
%X 
%Z 
%U 
%+
%^

%0 
%0 Thesis
%A Lin, D.
%D 1998
%T Fast algorithms for discovering the maximum frequent sets
%E 
%B 
%C 
%I University of New York
%V 
%6
%N 
%P 
%& 
%Y 
%S 
%7
%8 
%9 
%? 
%! 
%Z 
%@ 
%(
%)
%*
%L
%M
%1 
%2 fca
%3 phdthesis
%4 
%# 
%$ 
%F Lin98b
%K FCA, OntologyHandbook
%X 
%Z 
%U 
%+
%^

%0 
%0 Book Section
%A Han, J. & Fu, Y.
%D 1996
%T Exploration of the power of attribute oriented induction in data
    mining
%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 399--421
%& 
%Y 
%S 
%7 
%8 
%9 
%? 
%! 
%Z 
%@ 
%(
%)
%*
%L
%M
%1 
%2 fca
%3 incollection
%4 
%# 
%$ 
%F Han96a
%K FCA, OntologyHandbook
%X 
%Z 
%U 
%+
%^

%0 
%0 Thesis
%A John, G. H.
%D 1997
%T Enhancements to the data mining process
%E 
%B 
%C 
%I University of Stanford
%V 
%6
%N 
%P 
%& 
%Y 
%S 
%7
%8 
%9 
%? 
%! 
%Z 
%@ 
%(
%)
%*
%L
%M
%1 
%2 fca
%3 phdthesis
%4 
%# 
%$ 
%F John97
%K FCA, OntologyHandbook
%X 
%Z 
%U 
%+
%^

%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
%9
%? 
%! 
%Z 
%@ 
%(
%)
%*
%L
%M
%1 
%2 fca
%3 inproceedings
%4 
%# 
%$ 
%F Fayet98
%K FCA, OntologyHandbook
%X 
%Z 
%U 
%+
%^

%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 
%7
%8 May
%9
%? 
%! 
%Z 
%@ 
%(
%)
%*
%L
%M
%1 
%2 fca
%3 inproceedings
%4 
%# 
%$ 
%F Brin97a
%K FCA, OntologyHandbook
%X 
%Z 
%U 
%+
%^

%0 
%0 Journal Article
%A Cole, Richard J.; Eklund, Peter W. & Stumme, Gerd
%D 2003
%T Document Retrieval for Email Search and Discovery using Formal Concept Analysis
%E 
%B Journal of Applied Artificial Intelligence (AAI)
%C 
%I 
%V 17
%6
%N 3
%P 257-280
%& 
%Y 
%S 
%7
%8 
%9
%? 
%! 
%Z 
%@ 
%(
%)
%*
%L
%M
%1 
%2 Publications of Gerd Stumme
%3 article
%4 
%# 
%$ 
%F cole03document
%K 2003, analysis, concept, email, fca, formal, information, myown, nepomuk, retrieval, search
%X This paper discusses an document discovery tool based on
conceptual clustering by formal concept analysis. The program
allows users to navigate email using a visual lattice metaphor
rather than a tree. It implements a virtual file structure over
email where files and entire directories can appear in multiple
positions. The content and shape of the lattice formed by the
conceptual ontology can assist in email discovery. The system
described provides more flexibility in retrieving stored emails
than what is normally available in email clients. The paper
discusses how conceptual ontologies can leverage traditional
document retrieval systems and aid knowledge discovery in document
collections.
%Z 
%U http://www.kde.cs.uni-kassel.de/stumme/papers/2003/AAI03_emails.pdf
%+
%^

%0 
%0 Conference Proceedings
%A Koperski, K. & Han, J.
%D 1995
%T Discovery of spatial association rules in geographic information
    databases
%E 
%B Proceedings of the 4th international Symposium on advances in Spatial
    Databases (SSD'95)
%C 
%I Springer-Verlag
%V 
%6
%N 
%P 47--66
%& 
%Y 
%S Lecture Notes in Computer Science, Vol. 951
%7
%8 August
%9
%? 
%! 
%Z 
%@ 
%(
%)
%*
%L
%M
%1 
%2 fca
%3 inproceedings
%4 
%# 
%$ 
%F Koperski95
%K FCA, OntologyHandbook
%X 
%Z 
%U 
%+
%^

%0 
%0 Conference Proceedings
%A Han, J. & Fu, Y.
%D 1995
%T Discovery of multiple-level association rules from large databases
%E 
%B Proceedings of the 21st international conference on Very Large Data
    Bases (VLDB'95)
%C 
%I Morgan Kaufmann
%V 
%6
%N 
%P 420--431
%& 
%Y 
%S 
%7
%8 September
%9
%? 
%! 
%Z 
%@ 
%(
%)
%*
%L
%M
%1 
%2 fca
%3 inproceedings
%4 
%# 
%$ 
%F Han95
%K FCA, OntologyHandbook
%X 
%Z 
%U 
%+
%^

%0 
%0 Conference Proceedings
%A Lent, B.; Agrawal, R. & Srikant, R.
%D 1997
%T Discovering trends in text databases
%E 
%B Proceedings of the 3rd international conference on Knowledge Discovery
    and Data mining (KDD'97)
%C 
%I AAAI Press
%V 
%6
%N 
%P 227--230
%& 
%Y 
%S 
%7
%8 August
%9
%? 
%! 
%Z 
%@ 
%(
%)
%*
%L
%M
%1 
%2 fca
%3 inproceedings
%4 
%# 
%$ 
%F Lent97
%K FCA, OntologyHandbook
%X 
%Z 
%U 
%+
%^

%0 
%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
%C 
%I 
%V 6
%6
%N 1
%P 38-53
%& 
%Y 
%S 
%7
%8 
%9
%? 
%! 
%Z 
%@ 
%(
%)
%*
%L
%M
%1 
%2 
%3 article
%4 
%# 
%$ 
%F jaeschke08discovering
%K 2008, FCA, OntologyHandbook, analysis, bibsonomy, concept, discovering, fca, folksonomies, formal, itegpub, l3s, myown, shared, triadic
%X 
%Z 
%U http://dx.doi.org/10.1016/j.websem.2007.11.004
%+
%^

%0 
%0 Conference Proceedings
%A Gunopulos, D.; Mannila, H. & Saluja, S.
%D 1997
%T Discovering all most specific sentences by randomized algorithms
%E 
%B Proceedings of the 6th biennial International Conference on Database
    Theory (ICDT'97)
%C 
%I Springer-Verlag
%V 
%6
%N 
%P 215-229
%& 
%Y 
%S Lecture Notes in Computer Science, Vol. 1186
%7
%8 January
%9
%? 
%! 
%Z 
%@ 
%(
%)
%*
%L
%M
%1 
%2 fca
%3 inproceedings
%4 
%# 
%$ 
%F Gunopulos97b
%K FCA, OntologyHandbook
%X 
%Z 
%U 
%+
%^

%0 
%0 Journal Article
%A Godin, R.; Mili, H.; Mineau, G.; Missaoui, R.; Arfi, A. & Chau, T.
%D 1998
%T Design of class hierarchies based on concept (Galois) lattices.
%E 
%B TAPOS
%C 
%I 
%V 4
%6
%N 2
%P 117--134
%& 
%Y 
%S 
%7
%8 
%9
%? 
%! 
%Z 
%@ 
%(
%)
%*
%L
%M
%1 
%2 fca
%3 article
%4 
%# 
%$ 
%F Godin98design
%K FCA, OntologyHandbook
%X 
%Z 
%U 
%+
%^

%0 
%0 Conference Proceedings
%A Han, J.; Fu, Y.; Wang, W.; Chiang, J.; Gong, W.; Koperski, K.; Li, D.; Lu, Y.; Rajan, A.; Stefanovic, N.; Xia, B. & Za\"i,ane, O. R.
%D 1996
%T DBMiner : A system for mining knowledge in large relational
    databases
%E 
%B Proceedings of the 2nd international conference on Knowledge Discovery
    and Data mining (KDD'96)
%C 
%I AAAI Press
%V 
%6
%N 
%P 250--255
%& 
%Y 
%S 
%7
%8 August
%9
%? 
%! 
%Z 
%@ 
%(
%)
%*
%L
%M
%1 
%2 fca
%3 inproceedings
%4 
%# 
%$ 
%F Han96b
%K FCA, OntologyHandbook
%X 
%Z 
%U 
%+
%^

%0 
%0 Conference Proceedings
%A Han, J.; Chiang, J.; Chee, S.; Chen, J.; Chen, Q.; Cheng, S.; Gong, W.; Kamber, M.; Liu, K. Koperski G.; Lu, Y.; Stefanovic, N.; Winstone, L.; Xia, B.; Za\"i,ane, O. R.; Zhang, S. & Zhu, H.
%D 1997
%T DBMiner : A system for data mining in relational databases
    and data warehouses
%E 
%B Proceedings of the 1997 GASCON metting of minds (GASCON'97)
%C 
%I 
%V 
%6
%N 
%P 249--260
%& 
%Y 
%S 
%7
%8 November
%9
%? 
%! 
%Z 
%@ 
%(
%)
%*
%L
%M
%1 
%2 fca
%3 inproceedings
%4 
%# 
%$ 
%F Han97b
%K FCA, OntologyHandbook
%X 
%Z ftp://ftp.fas.sfu.ca/pub/cs/han/kdd/cascon97.ps
%U 
%+
%^

%0 
%0 Book
%A Han, J. & Kamber, M.
%D 2002
%T Data: Mining: Concepts and Techniques
%E 
%B 
%C 
%I Morgan Kaufmann
%V 
%6
%N 
%P
%& 
%Y 
%S 
%7 
%8 
%9
%? 
%! 
%Z 
%@ 
%(
%)
%*
%L
%M
%1 
%2 fca
%3 book
%4 
%# 
%$ 
%F Han02data
%K FCA, OntologyHandbook
%X 
%Z 
%U 
%+
%^

%0 
%0 Journal Article
%A Han, J.; Cai, Y. & Cercone, N.
%D 1993
%T Data-driven discovery of quantitative rules in relational databases
%E 
%B IEEE Transansaction on Knowledge and Data Engineering
%C 
%I IEEE Computer Society Press
%V 5
%6
%N 1
%P 29--40
%& 
%Y 
%S 
%7
%8 Febuary
%9
%? 
%! 
%Z 
%@ 
%(
%)
%*
%L
%M
%1 
%2 fca
%3 article
%4 
%# 
%$ 
%F Han93
%K FCA, OntologyHandbook
%X 
%Z 
%U 
%+
%^

%0 
%0 Book
%A Han, J. & Kamber, M.
%D 2000
%T Data Mining: Concepts and Techniques
%E 
%B 
%C 
%I Morgan Kaufmann
%V 
%6
%N 
%P
%& 
%Y 
%S 
%7 
%8 Sept.
%9
%? 
%! 
%Z 
%@ 
%(
%)
%*
%L
%M
%1 
%2 fca
%3 book
%4 
%# 
%$ 
%F Han:book00
%K FCA, OntologyHandbook
%X 
%Z 
%U 
%+
%^

%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
%N 
%P 
%& 
%Y 
%S 
%7
%8 
%9 
%? 
%! 
%Z 
%@ 
%(
%)
%*
%L
%M
%1 
%2 fca
%3 phdthesis
%4 
%# 
%$ 
%F Bastide00
%K FCA, OntologyHandbook
%X 
%Z 
%U 
%+
%^

%0 
%0 Conference Proceedings
%A Gunopulos, D.; Khardon, R.; Mannila, H. & Toivonen, H.
%D 1997
%T Data mining, hypergraph transversals, and machine learning
%E 
%B Proceedings of the 16th ACM SIGACT-SIGMOD-SIGART symposium on Principles
    of Database Systems (PODS'97)
%C 
%I ACM Press
%V 
%6
%N 
%P 209--216
%& 
%Y 
%S 
%7
%8 May
%9
%? 
%! 
%Z 
%@ 
%(
%)
%*
%L
%M
%1 
%2 fca
%3 inproceedings
%4 
%# 
%$ 
%F Gunopulos97a
%K FCA, OntologyHandbook
%X 
%Z 
%U 
%+
%^

%0 
%0 Conference Proceedings
%A Ganter, Bernhard & Stumme, Gerd
%D 2003
%T Creation and Merging of Ontology Top-Levels
%E de Moor, Aldo; Lex, Wilfried & Ganter, Bernhard
%B Conceptual Structures for Knowledge Creation and Communication.
%C Heidelberg
%I Springer
%V 2746
%6
%N 
%P 131-145
%& 
%Y 
%S LNAI
%7
%8 
%9
%? 
%! 
%Z 
%@ 
%(
%)
%*
%L
%M
%1 
%2 Publications of Gerd Stumme
%3 inproceedings
%4 
%# 
%$ 
%F ganter03creation
%K 2003, analysis, concept, fca, formal, merging, myown, ontologies, ontology
%X We provide a new method for systematically structuring the top-down level of ontologies.
It is based on an interactive, top--down knowledge acquisition
process, which assures that the knowledge engineer
considers all possible cases while avoiding redundant acquisition.
The method is suited especially for creating/merging the top
part(s) of the ontologies, where high accuracy is required, and for supporting the merging of two (or more) ontologies on that level.
%Z 
%U http://www.kde.cs.uni-kassel.de/stumme/papers/2003/ganter2003creation.pdf
%+
%^

%0 
%0 Journal Article
%A Kent, Robert E. & Neuss, Christian
%D 1995
%T Creating a Web Analysis and Visualization Environment
%E 
%B Computer Networks and ISDN Systems
%C 
%I 
%V 28
%6
%N 1\&2
%P 109-117
%& 
%Y 
%S 
%7
%8 
%9
%? 
%! 
%Z 
%@ 
%(
%)
%*
%L
%M
%1 
%2 fca
%3 article
%4 
%# 
%$ 
%F kent95creating
%K Analysis, Creating, FCA, OntologyHandbook, Visualization, Web, analysis, concept, creating, fca, formal, visualization, web
%X 
%Z 
%U http://dblp.uni-trier.de/db/journals/cn/cn28.html#KentN95
%+
%^

%0 
%0 Conference Proceedings
%A 
%D 2005
%T Contributions to ICCS 2005
%E Dau, Frithjof; Mugnier, Marie-Laure & Stumme, Gerd
%B Contributions to ICCS 2005
%C Kassel
%I kassel university press
%V 
%6
%N 
%P 
%& 
%Y 
%S 
%7
%8 
%9
%? 
%! 
%Z 
%@ 3-89958-138-5
%(
%)
%*
%L
%M
%1 
%2 Publications of Gerd Stumme
%3 proceedings
%4 
%# 
%$ 
%F dau05contributions
%K 2005, Germany, Kassel, analysis, concept, conceptual, conference, fca, formal, graphs, iccs, knowledge, l3s, myown, proceedings, sharing, structures
%X 
%Z 
%U http://www.kde.cs.uni-kassel.de/conf/iccs05
%+
%^

%0 
%0 Conference Proceedings
%A Kryszkiewicz, Marzena
%D 2001
%T Concise Representation of Frequent Patterns Based on Disjunction-Free
    Generators
%E 
%B ICDM '01: Proceedings of the 2001 IEEE International Conference on
    Data Mining
%C Washington, DC, USA
%I IEEE Computer Society
%V 
%6
%N 
%P 305--312
%& 
%Y 
%S 
%7
%8 
%9
%? 
%! 
%Z 
%@ 0-7695-1119-8
%(
%)
%*
%L
%M
%1 
%2 fca
%3 inproceedings
%4 
%# 
%$ 
%F kryszkiewicz01concise
%K FCA, OntologyHandbook
%X 
%Z 
%U 
%+
%^

%0 
%0 Conference Proceedings
%A 
%D 2005
%T Conceptual Structures: Common Semantics for Sharing Knowledge, 13th International Conference on Conceptual Structures, ICCS 2005, Kassel, Germany, July 17-22, 2005, Proceedings
%E Dau, Frithjof; Mugnier, Marie-Laure & Stumme, Gerd
%B ICCS
%C 
%I Springer
%V 3596
%6
%N 
%P 
%& 
%Y 
%S Lecture Notes in Computer Science
%7
%8 
%9
%? 
%! 
%Z 
%@ 3-540-27783-8
%(
%)
%*
%L
%M
%1 
%2 Publications of Gerd Stumme
%3 proceedings
%4 
%# 
%$ 
%F conf/iccs/2005
%K 2005, Germany, Kassel, analysis, concept, conceptual, conference, fca, formal, graphs, iccs, knowledge, l3s, myown, proceedings, sharing, structures
%X 
%Z 
%U http://www.kde.cs.uni-kassel.de/conf/iccs05
%+
%^

%0 
%0 Conference Proceedings
%A 
%D 2001
%T Conceptual Structures -- Broadening the Base. Proc. 9th International Conference on Conceptual Structures
%E Delugach, H. & Stumme, G.
%B 
%C Heidelberg
%I Springer
%V 2120
%6
%N 
%P 
%& 
%Y 
%S LNAI
%7
%8 
%9
%? 
%! 
%Z 
%@ 
%(
%)
%*
%L
%M
%1 
%2 Publications of Gerd Stumme
%3 proceedings
%4 
%# 
%$ 
%F delugach01conceptual
%K 2001, analysis, cg, cgs, concept, conceptual, fca, formal, graphs, iccs, myown, structures
%X 
%Z 
%U 
%+
%^

%0 
%0 Conference Proceedings
%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 Heidelberg
%I Springer
%V 2961
%6
%N 
%P 189-207
%& 
%Y Second International Conference on Formal Concept Analysis, ICFCA 2004
%S LNAI
%7
%8 
%9
%? 
%! 
%Z 
%@ 3-540-23258-3
%(
%)
%*
%L
%M
%1 
%2 Publications of Gerd Stumme
%3 inproceedings
%4 
%# 
%$ 
%F cimiano2004concept
%K 2004, ckp, conceptual, fca, itegpub, knowledge, l3s, myown, ontologies, processing
%X Among many other knowledge representations formalisms, Ontologies
and Formal Concept Analysis (FCA) aim at modeling 'concepts'. We
discuss how these two formalisms may complement another from an
application point of view. In particular, we will see how FCA can
be used to support Ontology Engineering, and how ontologies can be
exploited in FCA applications. The interplay of FCA and ontologies
is studied along the life cycle of an ontology:
 (i) FCA can support the building of the ontology as a
 learning technique.
 (ii) The established ontology can be analyzed and navigated by
 using techniques of FCA.
 (iii) Last but not least, the ontology may be used to improve an FCA
 application.
%Z 
%U http://www.kde.cs.uni-kassel.de/stumme/papers/2004/cimiano2004concept.pdf
%+
%^

%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
%(
%)
%*
%L
%M
%1 
%2 
%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 Journal Article
%A Hereth, Joachim; Stumme, Gerd; Wille, Rudolf & Wille, Uta
%D 2003
%T Conceptual Knowledge Discovery - a Human-Centered Approach
%E 
%B Journal of Applied Artificial Intelligence (AAI)
%C 
%I 
%V 17
%6
%N 3
%P 281-301
%& 
%Y 
%S 
%7
%8 
%9
%? 
%! 
%Z 
%@ 
%(
%)
%*
%L
%M
%1 
%2 Publications of Gerd Stumme
%3 article
%4 
%# 
%$ 
%F hereth03conceptual
%K 2003, analysis, concept, conceptual, discovery, fca, formal, human, kdd, knowledge, myown
%X In this paper we discuss Conceptual Knowledge Discovery in Databases (CKDD) as it is developing in the field of Conceptual Knowledge
 Processing. Conceptual Knowledge
 Processing is based on the mathematical theory of Formal Concept
 Analysis which has become a successful theory for data analysis during
 the last two decades. CKDD aims to support a human-centered process
of discovering knowledge from data by visualizing and analyzing
the conceptual structure of the data. We dicuss how the
management system TOSCANA for conceptual information systems
supports CKDD, and illustrate it by two applications in database
marketing and flight movement analysis. Finally, we present a
new tool for conceptual deviation discovery, Chianti.
%Z 
%U http://www.kde.cs.uni-kassel.de/stumme/papers/2003/hereth2003conceptual.pdf
%+
%^

%0 
%0 Conference Proceedings
%A Becker, K.; Stumme, G.; Wille, R.; Wille, U. & Zickwolff, M.
%D 2000
%T Conceptual Information Systems Discussed Through an IT-Security Tool
%E Dieng, R. & Corby, O.
%B Knowledge Engineering and Knowledge Management. Methods, Models, and Tools.
%C Heidelberg
%I Springer
%V 1937
%6
%N 
%P 352-365
%& 
%Y 
%S LNAI
%7
%8 
%9
%? 
%! 
%Z 
%@ 
%(
%)
%*
%L
%M
%1 
%2 alpha
%3 inproceedings
%4 
%# 
%$ 
%F becker00conceptual
%K 2000, OntologyHandbook, analysis, concept, conceptual, fca, formal, information, it, lattices, myown, security, system, systems, triadic
%X 
%Z 
%U http://www.kde.cs.uni-kassel.de/stumme/papers/2000/EKAW00.pdf
%+
%^

%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 
%9
%? 
%! 
%Z 
%@ 0169-023X
%(
%)
%*
%L
%M
%1 
%2 
%3 article
%4 
%# 
%$ 
%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 Journal Article
%A Kuznetsov, S. & Obiedkov, S.
%D 2002
%T Comparing performance of algorithms for generating concept lattices
%E 
%B Journal of Experimental and Theoretical Artificial Intelligence
%C 
%I 
%V 14
%6
%N 
%P 189-216
%& 
%Y 
%S 
%7
%8 
%9
%? 
%! 
%Z 
%@ 
%(
%)
%*
%L
%M
%1 
%2 fca
%3 article
%4 
%# 
%$ 
%F kuznetsov02comparing
%K FCA, OntologyHandbook, algorithm, algorithms, analysis, concept, fca, formal
%X 
%Z 
%U /brokenurl#citeseer.ist.psu.edu/666686.html
%+
%^

%0 
%0 Generic
%A Lambiotte, R. & Ausloos, M.
%D 2005
%T Collaborative tagging as a tripartite network
%E
%B 
%C
%I 
%V 
%6
%N 
%P 
%& 
%Y 
%S 
%7
%8 December
%9
%? 
%! 
%Z 
%@ 
%(
%)
%*
%L
%M
%1 
%2 fca
%3 misc
%4 
%# 
%$ 
%F citeulike:484851
%K FCA, OntologyHandbook, collaboration, discovery, folksonomy, tagging, taxonomy
%X We describe online collaborative communities by tripartite networks,
    the nodes being persons, items and tags. We introduce projection
    methods in order to uncover the structures of the networks, i.e.
    communities of users, genre families... <br />To do so, we focus
    on the correlations between the nodes, depending on their profiles,
    and use percolation techniques that consist in removing less correlated
    links and observing the shaping of disconnected islands. The structuring
    of the network is visualised by using a tree representation. The
    notion of diversity in the system is also discussed.
%Z {\tt arXiv:cs.DS/0512090}
%U http://arxiv.org/abs/cs.DS/0512090
%+
%^

%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 
%8 
%9
%? 
%! 
%Z 
%@ 
%(
%)
%*
%L
%M
%1 
%2 fca
%3 book
%4 
%# 
%$ 
%F Breiman84
%K FCA, OntologyHandbook
%X 
%Z 
%U 
%+
%^

%0 
%0 Conference Proceedings
%A Gardarin, G.; Pucheral, P. & Wu, F.
%D 1998
%T Bitmap based algorithms for mining association rules
%E 
%B Actes des 14�mes journ�es Bases de Donn�es Avanc�es (BDA'98)
%C 
%I 
%V 
%6
%N 
%P 157--175
%& 
%Y 
%S 
%7
%8 Octobre
%9
%? 
%! 
%Z 
%@ 
%(
%)
%*
%L
%M
%1 
%2 fca
%3 inproceedings
%4 
%# 
%$ 
%F Gardarin98
%K FCA, OntologyHandbook
%X 
%Z 
%U 
%+
%^

%0 
%0 Conference Proceedings
%A Hotho, Andreas; J\"a,schke, Robert; Schmitz, Christoph & Stumme, Gerd
%D 2006
%T BibSonomy: A Social Bookmark and Publication Sharing System
%E 
%B Proc. of the ICCS 2006 Conceptual Structures Tool Interoperability
    Workshop
%C 
%I 
%V 
%6
%N 
%P 
%& 
%Y 
%S 
%7
%8 
%9
%? 
%! 
%Z 
%@ 
%(
%)
%*
%L
%M
%1 
%2 fca
%3 inproceedings
%4 
%# 
%$ 
%F hjss06bibsonomy
%K FCA, OntologyHandbook, bibsonomy, bookmarking, folksonomy, iccs, social
%X 
%Z (to appear)
%U 
%+
%^

%0 
%0 Conference Proceedings
%A Hotho, Andreas; Jäschke, Robert; Schmitz, Christoph & Stumme, Gerd
%D 2006
%T BibSonomy: A Social Bookmark and Publication Sharing System
%E de Moor, Aldo; Polovina, Simon & Delugach, Harry
%B Proceedings of the First Conceptual Structures Tool Interoperability Workshop at the 14th International Conference on Conceptual Structures
%C Aalborg
%I Aalborg Universitetsforlag
%V 
%6
%N 
%P 87-102
%& 
%Y 
%S 
%7
%8 
%9
%? 
%! 
%Z 
%@ 87-7307-769-0
%(
%)
%*
%L
%M
%1 
%2 Publications of Gerd Stumme
%3 inproceedings
%4 
%# 
%$ 
%F hotho2006bibsonomy
%K 2006, FCA, OntologyHandbook, bibsonomy, bookmarking, folksonomy, iccs, l3s, myown, nepomuk, social, tagorapub
%X Social bookmark tools are rapidly emerging on the Web. In such
systems users are setting up lightweight conceptual structures
called folksonomies. The reason for their immediate success is the
fact that no specific skills are needed for participating. In this
paper we specify a formal model for folksonomies and briefly describe 
our own system BibSonomy, which allows for sharing both bookmarks
and publication references in a kind of personal library.
%Z 
%U http://www.kde.cs.uni-kassel.de/stumme/papers/2006/hotho2006bibsonomy.pdf
%+
%^

%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
%9
%? 
%! 
%Z 
%@ 
%(
%)
%*
%L
%M
%1 
%2 fca
%3 inproceedings
%4 
%# 
%$ 
%F Brin97b
%K FCA, OntologyHandbook
%X 
%Z 
%U 
%+
%^

%0 
%0 Book Section
%A Heckerman, D.
%D 1996
%T Bayesian networks for knowledge discovery
%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 273--305
%& 
%Y 
%S 
%7 
%8 
%9 
%? 
%! 
%Z 
%@ 
%(
%)
%*
%L
%M
%1 
%2 fca
%3 incollection
%4 
%# 
%$ 
%F Heckerman96
%K FCA, OntologyHandbook
%X 
%Z 
%U 
%+
%^

%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 
%7
%8 May
%9
%? 
%! 
%Z 
%@ 
%(
%)
%*
%L
%M
%1 
%2 
%3 inproceedings
%4 
%# 
%$ 
%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
%+
%^

%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 
%9
%? 
%! 
%Z 
%@ 3-540-41066-X
%(
%)
%*
%L
%M
%1 
%2 fca
%3 inproceedings
%4 
%# 
%$ 
%F boulicaut00approximation
%K FCA, OntologyHandbook
%X 
%Z 
%U 
%+
%^

%0 
%0 Journal Article
%A Godin, R. & Missaoui, R.
%D 1994
%T An incremental concept formation approach for learning from databases
%E 
%B Theoretical Computer Science : Special issue on formal methods in
    databases and software engineering
%C 
%I Elsevier Science
%V 133
%6
%N 2
%P 387--419
%& 
%Y 
%S 
%7
%8 October
%9
%? 
%! 
%Z 
%@ 
%(
%)
%*
%L
%M
%1 
%2 fca
%3 article
%4 
%# 
%$ 
%F Godin94
%K FCA, OntologyHandbook
%X 
%Z 
%U 
%+
%^

%0 
%0 Conference Proceedings
%A Gupta, A.; Harinarayan, V. & Quass, D.
%D 1995
%T Aggregate-query processing in data warehousing environments
%E 
%B Proceedings of the 21st international conference on Very Large Data
    Bases (VLDB'95)
%C 
%I Morgan Kaufmann
%V 
%6
%N 
%P 358--369
%& 
%Y 
%S 
%7
%8 September
%9
%? 
%! 
%Z 
%@ 
%(
%)
%*
%L
%M
%1 
%2 fca
%3 inproceedings
%4 
%# 
%$ 
%F Gupta95
%K FCA, OntologyHandbook
%X 
%Z 
%U 
%+
%^

%0 
%0 Conference Proceedings
%A 
%D 2002
%T Advances in Formal Concept Analysis for Knowledge Discovery in Databases. Proc. Workshop FCAKDD of the 15th European
                  Conference on Artificial Intelligence (ECAI 2002)
%E Duquenne, V.; Ganter, B.; Liquiere, M.; Nguifo, E. M. & Stumme, G.
%B 
%C Lyon, France
%I 
%V 
%6
%N 
%P 
%& 
%Y 
%S 
%7
%8 July 23,
%9
%? 
%! 
%Z 
%@ 
%(
%)
%*
%L
%M
%1 
%2 Publications of Gerd Stumme
%3 proceedings
%4 
%# 
%$ 
%F duquenne02advanced
%K 2002, analysis, concept, ecai, fca, formal, myown, proceedings, workshop
%X 
%Z 
%U http://www.lirmm.fr/~liquiere/Documents/FCAKDDProceedings2002.pdf
%+
%^

%0 
%0 Conference Proceedings
%A Ferr�, S. & Ridoux, O.
%D 2000
%T A Logical Generalization of Formal Concept Analysis
%E Mineau, G. & Ganter, B.
%B Int. Conf. Conceptual Structures
%C 
%I Springer
%V 
%6
%N 
%P 371--384
%& 
%Y 
%S LNCS 1867
%7
%8 
%9
%? 
%! 
%Z 
%@ 
%(
%)
%*
%L
%M
%1 
%2 fca
%3 inproceedings
%4 
%# 
%$ 
%F ferre00logical
%K FCA, OntologyHandbook, concept_analysis, context, information_system, logic
%X We propose a generalization of Formal Concept Analysis (FCA) in which
    sets of attributes are replaced by expressions of an almost arbitrary
    logic. We prove that all FCA can be reconstructed on this basis.
    We show that from any logic that is used in place of sets of attributes
    can be derived a contextualized logic that takes into account the
    formal context and that is isomorphic to the concept lattice. We
    then justify the generalization of FCA compared with existing extensions
    and in the perspective of its application to information systems.
%Z 
%U 
%+
%^

%0 
%0 Journal Article
%A Carpineto, Claudio & Romano, Giovanni
%D 1996
%T A lattice conceptual clustering system and its application to browsing retrieval
%E 
%B Machine Learning
%C 
%I 
%V 24
%6
%N 2
%P 95--122
%& 
%Y 
%S 
%7
%8 August
%9
%? 
%! 
%Z 
%@ 
%(
%)
%*
%L
%M
%1 
%2 fca
%3 article
%4 
%# 
%$ 
%F keyhere
%K analysis, carpineto, clustering, concept, fca, formal, information, ir, retrieval
%X The theory of concept (or Galois) lattices provides a simple and formal approach to conceptual clustering. In this paper we present GALOIS, a system that automates and applies this theory. The algorithm utilized by GALOIS to build a concept lattice is incremental and efficient, each update being done in time at most quadratic in the number of objects in the lattice. Also, the algorithm may incorporate background information into the lattice, and through clustering, extend the scope of the theory. The application we present is concerned with information retrieval via browsing, for which we argue that concept lattices may represent major support structures. We describe a prototype user interface for browsing through the concept lattice of a document-term relation, possibly enriched with a thesaurus of terms. An experimental evaluation of the system performed on a medium-sized bibliographic database shows good retrieval performance and a significant improvement after the introduction of background knowledge.
ER  -
%Z 
%U http://dx.doi.org/10.1007/BF00058654
%+
%^

%0 
%0 Conference Proceedings
%A Stumme, Gerd
%D 2005
%T A Finite State Model for On-Line Analytical Processing in
               Triadic Contexts
%E Ganter, Bernhard & Godin, Robert
%B Proc. 3rd Intl. Conf. on Formal Concept Analysis
%C Heidelberg
%I Springer
%V 3403
%6
%N 
%P 315-328
%& 
%Y 
%S Lecture Notes in Computer Science
%7
%8 
%9
%? 
%! 
%Z 
%@ 3-540-24525-1
%(
%)
%*
%L
%M
%1 
%2 Publications of Gerd Stumme
%3 inproceedings
%4 
%# 
%$ 
%F stumme05finite
%K 2005, fca, folksonomies, itegpub, l3s, myown, olap, triadic
%X 
%Z 
%U http://www.kde.cs.uni-kassel.de/stumme/papers/2005/stumme2005finite.pdf
%+
%^

%0 
%0 Conference Proceedings
%A Klemettinen, M.; Mannila, H. & Toivonen, H.
%D 1997
%T A data-mining methodology and its application to semi-automatic knowledge
    acquisition
%E 
%B Proceedings of the 8th international conference on Database and Expert
    systems Applications (DEXA'97)
%C 
%I Springer-Verlag
%V 
%6
%N 
%P 670--677
%& 
%Y 
%S Lecture Notes in Computer Science, Vol. 1308
%7
%8 September
%9
%? 
%! 
%Z 
%@ 
%(
%)
%*
%L
%M
%1 
%2 fca
%3 inproceedings
%4 
%# 
%$ 
%F Klemettinen97
%K FCA, OntologyHandbook
%X 
%Z 
%U 
%+
%^

%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
%N 
%P 
%& 
%Y 
%S 
%7
%8 
%9
%? 
%! 
%Z 
%@ 
%(
%)
%*
%L
%M
%1 
%2 fca
%3 inproceedings
%4 
%# 
%$ 
%F Bykowski01condensed
%K FCA, OntologyHandbook, dblp
%X 
%Z 
%U http://dblp.uni-trier.de/db/conf/pods/pods2001.html#BykowskiR01
%+
%^