**Attribute Exploration on the Web**.

In: P. Cellier, F. Distel and B. Ganter, editors,

*Contributions to the 11th International Conference on Formal Concept Analysis*, pages 19-34.
2013.

Robert Jäschke and Sebastian Rudolph.

[doi]
[abstract]
[BibTeX]
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.

**Publication Analysis of the Formal Concept Analysis Community**.

In: F. Domenach, D. Ignatov and J. Poelmans, editors,

*ICFCA 2012*, volume 7278, series Lecture Notes in Artificial Intelligence, pages 77-95.
Springer, Berlin/Heidelberg, 2012.

Stephan Doerfel, Robert Jäschke and Gerd Stumme.

[doi]
[abstract]
[BibTeX]
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.

**Text Mining Scientific Papers: A Survey on FCA-Based Information Retrieval Research**.

In:
P. Perner, editor,

*Advances in Data Mining. Applications and Theoretical Aspects*, pages 273-287.
Springer Berlin Heidelberg, 2012.

Jonas Poelmans, DmitryI. Ignatov, Stijn Viaene, Guido Dedene and SergeiO. Kuznetsov.

[doi]
[abstract]
[BibTeX]
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.

**Information Retrieval in Folksonomies: Search and Ranking**.

In:

*Proceedings of the 3rd European Semantic Web Conference*, series Lecture Notes in Computer Science, pages 411-426.
Springer, 2006.

Andreas Hotho, Robert Jäschke, Christoph Schmitz and Gerd Stumme.

[BibTeX]

**Conceptual Knowledge Processing with Formal Concept Analysis and Ontologies**.

In:
P. Eklund, editor,

*Concept Lattices*, pages 189-207.
Springer, Berlin/Heidelberg, 2004.

Philipp Cimiano, Andreas Hotho, Gerd Stumme and Julien Tane.

[doi]
[BibTeX]

**Mining All Non-derivable Frequent Itemsets**.

In:

*PKDD '02: Proceedings of the 6th European Conference on Principles
of Data Mining and Knowledge Discovery*, pages 74-85.
Springer-Verlag, London, UK, 2002.

Toon Calders and Bart Goethals.

[BibTeX]

**Computing iceberg concept lattices with TITANIC**.

*Data & Knowledge Engineering*, 42(2):189-222, 2002.

Gerd Stumme, Rafik Taouil, Yves Bastide, Nicolas Pasquier and Lotfi Lakhal.

[doi]
[abstract]
[BibTeX]
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.

**MAFIA: A maximal frequent itemset algorithm for transactional databases**.

In:

*Proc. of the 17th Int. Conf. on Data Engineering*.
IEEE Computer Society, 2001.

D. Burdick, M. Calimlim and J. Gehrke.

[BibTeX]

**A condensed representation to find frequent patterns.**.

In:

*PODS*.
2001.

Artur Bykowski and Christophe Rigotti.

[doi]
[BibTeX]

**Data Mining: algorithmes par niveau, techniques d'implementation
et applications**.

PhD thesis, Université de Clermont-Ferrand II, 2000.

Y. Bastide.

[BibTeX]

**Approximation of Frequency Queries by Means of Free-Sets**.

In:

*PKDD '00: Proceedings of the 4th European Conference on Principles
of Data Mining and Knowledge Discovery*, pages 75-85.
Springer-Verlag, London, UK, 2000.

Jean-Francois Boulicaut, Artur Bykowski and Christophe Rigotti.

[BibTeX]

**Formal concept analysis with ConImp : Introduction to the
basic features**.

Technische Hochschule Darmstadt, 1998. http://www.mathematik.tu-darmstadt.de/~burmeister/ConImpIntro.ps.

P. Burmeister.

[BibTeX]

**Découverte de r�gles pertinentes dans les bases de donn�es**.

In:

*Actes des 14�mes journ�es Bases de Donn�es Avanc�es (BDA'98)*, pages 197-211.
1998.

A. Fayet, A. Giacometti, D. Laurent and N. Spyratos.

[BibTeX]

**Beyond market baskets: Generalizing association rules to correlation**.

In:

*Proceedings of the 1997 ACM SIGMOD international conference on Management
of Data (SIGMOD'97)*, pages 265-276.
ACM Press, 1997.

S. Brin, R. Motwani and C. Silverstein.

[BibTeX]

**Dynamic itemset counting and implication rules for market basket
data**.

In:

*Proceedings of the 1997 ACM SIGMOD international conference on Management
of Data (SIGMOD'97)*, pages 255-264.
ACM Press, 1997.

S. Brin, R. Motwani, J. D. Ullman and S. Tsur.

[BibTeX]

**From data mining to knowledge discovery : An overview**.

In:
U. M. Fayyad, G. Piatetsky-Shapiro, P. Smyth and R. Uthurusamy, editors,

*Advances in Knowledge Discovery and Data Mining*, pages 1-30.
AAAI Press, 1996.

U. M. Fayyad, G. Piatetsky-Shapiro and P. Smyth.

[BibTeX]

**Knowledge discovery and data mining : Towards a unifying framework**.

In:

*Proceedings of the 2nd international conference on Knowledge Discovery
and Data mining (KDD'96)*, pages 82-88.
AAAI Press, 1996.

U. M. Fayyad, G. Piatetsky-Shapiro and P. Smyth.

[BibTeX]

**Knowledge discovery in textual databases**.

In:

*Proceedings of the 1st international conference on Knowledge Discovery
and Data mining (KDD'95)*, pages 112-117.
AAAI Press, 1995.

R. Feldman and I. Dagan.

[BibTeX]

**Classification and regression trees**.

1984.

L. Breiman, J. H. Friedman, R. A. Olshen and C. J. Stone.

[BibTeX]

**Philosophisches W�rterbuch**.

1976.

W. Brugger.

[BibTeX]