@incollection{springerlink:10.1007/978-3-642-14197-3_15,
  abstract = {In this paper, we analyze the literature on Formal Concept Analysis (FCA) using FCA. We collected 702 papers published between 2003-2009 mentioning Formal Concept Analysis in the abstract. We developed a knowledge browsing environment to support our literature analysis process. The pdf-files containing the papers were converted to plain text and indexed by Lucene using a thesaurus containing terms related to FCA research. We use the visualization capabilities of FCA to explore the literature, to discover and conceptually represent the main research topics in the FCA community. As a case study, we zoom in on the 140 papers on using FCA in knowledge discovery and data mining and give an extensive overview of the contents of this literature.},
  address = {Berlin / Heidelberg},
  affiliation = {K.U.Leuven, Faculty of Business and Economics Naamsestraat 69 3000 Leuven Belgium},
  author = {Poelmans, Jonas and Elzinga, Paul and Viaene, Stijn and Dedene, Guido},
  booktitle = {Conceptual Structures: From Information to Intelligence},
  doi = {10.1007/978-3-642-14197-3_15},
  editor = {Croitoru, Madalina and Ferré, Sébastien and Lukose, Dickson},
  interhash = {713d63f847ff4b2cbf613fc0508eb31b},
  intrahash = {9694689a034cc02aae1e27114ca26a94},
  isbn = {978-3-642-14196-6},
  keyword = {Computer Science},
  pages = {139-153},
  publisher = {Springer},
  series = {Lecture Notes in Computer Science},
  title = {Formal Concept Analysis in Knowledge Discovery: A Survey},
  url = {http://dx.doi.org/10.1007/978-3-642-14197-3_15},
  volume = 6208,
  year = 2010
}

@incollection{poelmans2010formal,
  abstract = {In this paper, we analyze the literature on Formal Concept Analysis (FCA) using FCA. We collected 702 papers published between 2003-2009 mentioning Formal Concept Analysis in the abstract. We developed a knowledge browsing environment to support our literature analysis process. The pdf-files containing the papers were converted to plain text and indexed by Lucene using a thesaurus containing terms related to FCA research. We use the visualization capabilities of FCA to explore the literature, to discover and conceptually represent the main research topics in the FCA community. As a case study, we zoom in on the 140 papers on using FCA in knowledge discovery and data mining and give an extensive overview of the contents of this literature.},
  address = {Berlin/Heidelberg},
  author = {Poelmans, Jonas and Elzinga, Paul and Viaene, Stijn and Dedene, Guido},
  booktitle = {Conceptual Structures: From Information to Intelligence},
  doi = {10.1007/978-3-642-14197-3_15},
  editor = {Croitoru, Madalina and Ferré, Sébastien and Lukose, Dickson},
  interhash = {713d63f847ff4b2cbf613fc0508eb31b},
  intrahash = {9694689a034cc02aae1e27114ca26a94},
  isbn = {978-3-642-14196-6},
  pages = {139--153},
  publisher = {Springer},
  series = {Lecture Notes in Computer Science},
  title = {Formal Concept Analysis in Knowledge Discovery: A Survey},
  url = {http://dx.doi.org/10.1007/978-3-642-14197-3_15},
  volume = 6208,
  year = 2010
}

@inproceedings{poelmans2011mining,
  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 Elzinga, Paul and Viaene, Stijn and Dedene, Guido and Kuznetsov, Sergei O.},
  booktitle = {Industrial Conference on Data Mining - Poster and Industry Proceedings},
  editor = {Perner, Petra},
  interhash = {b44d11ea5b5a4df8ee30a9c572d82051},
  intrahash = {164c37be60c1a47d1727ad9b82f01237},
  isbn = {978-3-942954-06-4},
  pages = {82--96},
  publisher = {IBaI Publishing},
  title = {Text Mining Scientific Papers: a Survey on {FCA}-based Information Retrieval Research.},
  url = {http://dblp.uni-trier.de/db/conf/incdm/incdm2011p.html#PoelmansEVDK11},
  year = 2011
}

@incollection{noKey,
  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.},
  booktitle = {Advances in Data Mining. Applications and Theoretical Aspects},
  doi = {10.1007/978-3-642-31488-9_22},
  editor = {Perner, Petra},
  interhash = {04d543b37049a90f2cbd796585bda214},
  intrahash = {f6eddba1f2c6b7cdbfa67a0c79ae5ae8},
  isbn = {978-3-642-31487-2},
  language = {English},
  pages = {273-287},
  publisher = {Springer Berlin Heidelberg},
  series = {Lecture Notes in Computer Science},
  title = {Text Mining Scientific Papers: A Survey on FCA-Based Information Retrieval Research},
  url = {http://dx.doi.org/10.1007/978-3-642-31488-9_22},
  volume = 7377,
  year = 2012
}

@article{poelmans2013formal1,
  abstract = {Abstract This is the first part of a large survey paper in which we analyze recent literature on Formal Concept Analysis (FCA) and some closely related disciplines using FCA. We collected 1072 papers published between 2003 and 2011 mentioning terms related to Formal Concept Analysis in the title, abstract and keywords. We developed a knowledge browsing environment to support our literature analysis process. We use the visualization capabilities of \{FCA\} to explore the literature, to discover and conceptually represent the main research topics in the \{FCA\} community. In this first part, we zoom in on and give an extensive overview of the papers published between 2003 and 2011 on developing FCA-based methods for knowledge processing. We also give an overview of the literature on \{FCA\} extensions such as pattern structures, logical concept analysis, relational concept analysis, power context families, fuzzy FCA, rough FCA, temporal and triadic concept analysis and discuss scalability issues. },
  author = {Poelmans, Jonas and Kuznetsov, Sergei O. and Ignatov, Dmitry I. and Dedene, Guido},
  doi = {http://dx.doi.org/10.1016/j.eswa.2013.05.007},
  interhash = {7c70b9621e962c7688ee8ea2670af869},
  intrahash = {ba0e43c2318a5433d9a3d9a253dea6ca},
  issn = {0957-4174},
  journal = {Expert Systems with Applications },
  number = 16,
  pages = {6601 - 6623},
  title = {Formal Concept Analysis in knowledge processing: A survey on models and techniques },
  url = {http://www.sciencedirect.com/science/article/pii/S0957417413002935},
  volume = 40,
  year = 2013
}

@article{poelmans2013formal2,
  abstract = {Abstract This is the second part of a large survey paper in which we analyze recent literature on Formal Concept Analysis (FCA) and some closely related disciplines using FCA. We collected 1072 papers published between 2003 and 2011 mentioning terms related to Formal Concept Analysis in the title, abstract and keywords. We developed a knowledge browsing environment to support our literature analysis process. We use the visualization capabilities of \{FCA\} to explore the literature, to discover and conceptually represent the main research topics in the \{FCA\} community. In this second part, we zoom in on and give an extensive overview of the papers published between 2003 and 2011 which applied FCA-based methods for knowledge discovery and ontology engineering in various application domains. These domains include software mining, web analytics, medicine, biology and chemistry data. },
  author = {Poelmans, Jonas and Ignatov, Dmitry I. and Kuznetsov, Sergei O. and Dedene, Guido},
  doi = {http://dx.doi.org/10.1016/j.eswa.2013.05.009},
  interhash = {2a69c00103c8bde11fccfb70efcd16d0},
  intrahash = {fdfbf5941c34042ddcb5fbbb58bfe9c1},
  issn = {0957-4174},
  journal = {Expert Systems with Applications },
  number = 16,
  pages = {6538 - 6560},
  title = {Formal concept analysis in knowledge processing: A survey on applications },
  url = {http://www.sciencedirect.com/science/article/pii/S0957417413002959},
  volume = 40,
  year = 2013
}