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    Poelmans, J., Ignatov, D., Viaene, S., Dedene, G. & Kuznetsov, S. Text Mining Scientific Papers: A Survey on FCA-Based Information Retrieval Research 2012
    Vol. 7377Advances in Data Mining. Applications and Theoretical Aspects, pp. 273-287 
    incollection DOI URL 
    Abstract: 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.
      author = {Poelmans, Jonas and Ignatov, DmitryI. and Viaene, Stijn and Dedene, Guido and Kuznetsov, SergeiO.},
      title = {Text Mining Scientific Papers: A Survey on FCA-Based Information Retrieval Research},
      booktitle = {Advances in Data Mining. Applications and Theoretical Aspects},
      publisher = {Springer Berlin Heidelberg},
      year = {2012},
      volume = {7377},
      pages = {273-287},
      url = {http://dx.doi.org/10.1007/978-3-642-31488-9_22},
      doi = {http://dx.doi.org/10.1007/978-3-642-31488-9_22}
    Doerfel, S., Jäschke, R. & Stumme, G. Publication Analysis of the Formal Concept Analysis Community 2012
    Vol. 7278ICFCA 2012, pp. 77-95 
    inproceedings URL 
    Abstract: 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.
      author = {Doerfel, Stephan and Jäschke, Robert and Stumme, Gerd},
      title = {Publication Analysis of the Formal Concept Analysis Community},
      booktitle = {ICFCA 2012},
      publisher = {Springer},
      year = {2012},
      volume = {7278},
      pages = {77--95},
      url = {https://www.kde.cs.uni-kassel.de/pub/pdf/doerfel2012publication.pdf}
    Cimiano, P., Hotho, A., Stumme, G. & Tane, J. Conceptual Knowledge Processing with Formal Concept Analysis and Ontologies 2004
    Vol. 2961Concept Lattices, pp. 189-207 
    incollection URL 
      author = {Cimiano, Philipp and Hotho, Andreas and Stumme, Gerd and Tane, Julien},
      title = {Conceptual Knowledge Processing with Formal Concept
    Analysis and Ontologies}, booktitle = {Concept Lattices}, publisher = {Springer}, year = {2004}, volume = {2961}, pages = {189--207}, url = {http://dx.doi.org/10.1007/978-3-540-24651-0_18} }
    Stumme, G., Taouil, R., Bastide, Y., Pasquier, N. & Lakhal, L. Computing iceberg concept lattices with TITANIC 2002 Data & Knowledge Engineering
    Vol. 42(2), pp. 189-222 
    article DOI URL 
    Abstract: 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.
      author = {Stumme, Gerd and Taouil, Rafik and Bastide, Yves and Pasquier, Nicolas and Lakhal, Lotfi},
      title = {Computing iceberg concept lattices with TITANIC},
      journal = {Data & Knowledge Engineering},
      publisher = {Elsevier Science Publishers B. V.},
      year = {2002},
      volume = {42},
      number = {2},
      pages = {189--222},
      url = {http://portal.acm.org/citation.cfm?id=606457},
      doi = {http://dx.doi.org/10.1016/S0169-023X(02)00057-5}
    Jäschke, R. & Rudolph, S. Attribute Exploration on the Web 2013 Contributions to the 11th International Conference on Formal Concept Analysis, pp. 19-34  inproceedings URL 
    Abstract: 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.
      author = {Jäschke, Robert and Rudolph, Sebastian},
      title = {Attribute Exploration on the Web},
      booktitle = {Contributions to the 11th International Conference on Formal Concept Analysis},
      year = {2013},
      pages = {19--34},
      url = {http://nbn-resolving.de/urn:nbn:de:bsz:14-qucosa-113133}

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