@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{jaeschke2012challenges,
  abstract = {Originally introduced by social bookmarking systems, collaborative tagging, or social tagging, has been widely adopted by many web-based systems like wikis, e-commerce platforms, or social networks. Collaborative tagging systems allow users to annotate resources using freely chosen keywords, so called tags . Those tags help users in finding/retrieving resources, discovering new resources, and navigating through the system. The process of tagging resources is laborious. Therefore, most systems support their users by tag recommender components that recommend tags in a personalized way. The Discovery Challenges 2008 and 2009 of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD) tackled the problem of tag recommendations in collaborative tagging systems. Researchers were invited to test their methods in a competition on datasets from the social bookmark and publication sharing system BibSonomy. Moreover, the 2009 challenge included an online task where the recommender systems were integrated into BibSonomy and provided recommendations in real time. In this chapter we review, evaluate and summarize the submissions to the two Discovery Challenges and thus lay the groundwork for continuing research in this area.},
  address = {Berlin/Heidelberg},
  affiliation = {Knowledge & Data Engineering Group, University of Kassel, Wilhelmshöher Allee 73, 34121 Kassel, Germany},
  author = {Jäschke, Robert and Hotho, Andreas and Mitzlaff, Folke and Stumme, Gerd},
  booktitle = {Recommender Systems for the Social Web},
  doi = {10.1007/978-3-642-25694-3_3},
  editor = {Pazos Arias, José J. and Fernández Vilas, Ana and Díaz Redondo, Rebeca P.},
  interhash = {75b1a6f54ef54d0126d0616b5bf77563},
  intrahash = {7d41d332cccc3e7ba8e7dadfb7996337},
  isbn = {978-3-642-25694-3},
  pages = {65--87},
  publisher = {Springer},
  series = {Intelligent Systems Reference Library},
  title = {Challenges in Tag Recommendations for Collaborative Tagging Systems},
  url = {http://dx.doi.org/10.1007/978-3-642-25694-3_3},
  volume = 32,
  year = 2012
}

@incollection{fayyad1996data,
  abstract = {Data mining and knowledge discovery in
databases have been attracting a significant amount of research, industry, and media attention of late. What is all the excitement about? This article provides an overview of this emerging
field, clarifying how data mining and knowledge discovery in databases are related both to each other and to related fields, such as machine learning, statistics, and databases. The article
mentions particular real-world applications, specific data-mining techniques, challenges involved in real-world applications of knowledge
discovery, and current and future research directions in the field.},
  address = {Menlo Park, CA, USA},
  author = {Fayyad, Usama M. and Piatetsky-Shapiro, Gregory and Smyth, Padhraic},
  booktitle = {Advances in knowledge discovery and data mining},
  editor = {Fayyad, Usama M. and Piatetsky-Shapiro, Gregory and Smyth, Padhraic and Uthurusamy, Ramasamy},
  interhash = {79663e4b1f464b82ce1ae45345dc424f},
  intrahash = {3f5a400d01a974f993cee1ac5f79cfc8},
  isbn = {0-262-56097-6},
  pages = {1--34},
  publisher = {American Association for Artificial Intelligence},
  title = {From data mining to knowledge discovery: an overview},
  url = {http://portal.acm.org/citation.cfm?id=257942},
  year = 1996
}

@article{atzmueller2005semiautomatic,
  abstract = {Visual mining methods enable the direct integration of the user to overcome major problems of automatic data mining methods, e.g., the presentation of uninteresting results, lack of acceptance of the discovered findings, or limited confidence in these. We present a novel subgroup mining approach for explorative and descriptive data mining implemented in the VIKAMINE system. We propose several integrated visualization methods to support subgroup mining. Furthermore, we describe three case studies using data from fielded systems in the medical domain.},
  author = {Atzmüller, Martin and Puppe, Frank},
  date = {2006-04-19},
  doi = {10.3217/jucs-011-11-1752},
  interhash = {0b67dd9c2f5aacb51b76de3e2b9a2116},
  intrahash = {441e7f60d02f2fd1ec0eb913545a4c89},
  journal = {Journal of Universal Computer Science},
  number = 11,
  pages = {1752-1765},
  title = {Semi-Automatic Visual Subgroup Mining using VIKAMINE.},
  url = {http://www.jucs.org/jucs_11_11/semi_automatic_visual_subgroup},
  volume = 11,
  year = 2005
}

@inproceedings{atzmueller2006sdmap,
  abstract = {In this paper we present the novel SD-Map algorithm for exhaustive but efficient subgroup discovery. SD-Map guarantees to identify all interesting subgroup patterns contained in a data set, in contrast to heuristic or samplingbased methods. The SD-Map algorithm utilizes the well-known FP-growth method for mining association rules with adaptations for the subgroup discovery task.We show how SD-Map can handle missing values, and provide an experimental evaluation of the performance of the algorithm using synthetic data.},
  author = {Atzmüller, Martin and Puppe, Frank},
  booktitle = {PKDD},
  crossref = {conf/pkdd/2006},
  date = {2006-10-23},
  doi = {10.1007/11871637_6},
  editor = {Fürnkranz, Johannes and Scheffer, Tobias and Spiliopoulou, Myra},
  interhash = {f856c6b72f915861203f4ab460dc247d},
  intrahash = {3edb41b31504187c963dd0383ee379e6},
  isbn = {3-540-45374-1},
  pages = {6-17},
  publisher = {Springer},
  series = {Lecture Notes in Computer Science},
  title = {SD-Map - A Fast Algorithm for Exhaustive Subgroup Discovery.},
  url = {http://dblp.uni-trier.de/db/conf/pkdd/pkdd2006.html#AtzmullerP06},
  volume = 4213,
  year = 2006
}