@article{poelmans2012semiautomated,
  abstract = {We propose an iterative and human-centred knowledge discovery methodology based on formal concept analysis. The proposed approach recognizes the important role of the domain expert in mining real-world enterprise applications and makes use of specific domain knowledge, including human intelligence and domain-specific constraints. Our approach was empirically validated at the Amsterdam-Amstelland police to identify suspects and victims of human trafficking in 266,157 suspicious activity reports. Based on guidelines of the Attorney Generals of the Netherlands, we first defined multiple early warning indicators that were used to index the police reports. Using concept lattices, we revealed numerous unknown human trafficking and loverboy suspects. In-depth investigation by the police resulted in a confirmation of their involvement in illegal activities resulting in actual arrestments been made. Our human-centred approach was embedded into operational policing practice and is now successfully used on a daily basis to cope with the vastly growing amount of unstructured information.},
  author = {Poelmans, Jonas and Elzinga, Paul and Ignatov, Dmitry I. and Kuznetsov, Sergei O.},
  doi = {10.1080/03081079.2012.721662},
  eprint = {http://www.tandfonline.com/doi/pdf/10.1080/03081079.2012.721662},
  interhash = {18d6f6312af57cc72d7e26de4903dc9f},
  intrahash = {9bb41c50dd5333f94a807482489c0732},
  journal = {International Journal of General Systems},
  number = 8,
  pages = {774--804},
  title = {Semi-automated knowledge discovery: identifying and profiling human trafficking},
  url = {http://www.tandfonline.com/doi/abs/10.1080/03081079.2012.721662},
  volume = 41,
  year = 2012
}

@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{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
}

@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
}