@inproceedings{stumme01conceptualclustering, address = {Universität Dortmund 763}, author = {Stumme, G. and Taouil, R. and Bastide, Y. and Lakhal, L.}, booktitle = {Proc. GI-Fachgruppentreffen Maschinelles Lernen (FGML'01)}, editor = {Klinkenberg, R. and Rüping, S. and Fick, A. and Henze, N. and Herzog, C. and Molitor, R. and Schröder, O.}, interhash = {c99f2ae002435208c58f9244d298a10b}, intrahash = {f4ec21d5f63dbc213a3a6eae076c4b62}, month = {October}, title = {Conceptual Clustering with Iceberg Concept Lattices}, url = {http://www.kde.cs.uni-kassel.de/stumme/papers/2001/FGML01.pdf}, year = 2001 } @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 } @article{pasquier2005generating, author = {Pasquier, Nicolas and Taouil, Rafik and Bastide, Yves and Stumme, Gerd and Lakhal, Lotfi}, comment = {alpha}, interhash = {cb0ee99fae39f2a5e0af5be9d97978f5}, intrahash = {40f59a7fa7ce5015f9ee81709db89de0}, journal = {Journal Intelligent Information Systems (JIIS)}, number = 1, pages = {29-60}, publisher = {Kluwer Academic Publishers}, title = {Generating a Condensed Representation for Association Rules}, url = {http://www.kde.cs.uni-kassel.de/stumme/papers/2005/pasquier2005generating.pdf}, volume = 24, year = 2005 } @inbook{lakhal2005efficient, abstract = {Association rules are a popular knowledge discovery technique for warehouse basket analysis. They indicate which items of the warehouse are frequently bought together. The problem of association rule mining has first been stated in 1993. Five years later, several research groups discovered that this problem has a strong connection to Formal Concept Analysis (FCA). In this survey, we will first introduce some basic ideas of this connection along a specific algorithm, \titanic, and show how FCA helps in reducing the number of resulting rules without loss of information, before giving a general overview over the history and state of the art of applying FCA for association rule mining.}, address = {Heidelberg}, author = {Lakhal, Lotfi and Stumme, Gerd}, booktitle = {Formal Concept Analysis: Foundations and Applications}, editor = {Ganter, Bernhard and Stumme, Gerd and Wille, Rudolf}, ee = {http://dx.doi.org/10.1007/11528784_10}, interhash = {f5777a0f9dccfcf4f9968119d77297fc}, intrahash = {2b350f817428e4c6c7259cd279815091}, pages = {180-195}, publisher = {Springer}, series = {LNAI}, title = {Efficient Mining of Association Rules Based on Formal Concept Analysis}, url = {http://www.kde.cs.uni-kassel.de/stumme/papers/2005/lakhal2005efficient.pdf}, volume = 3626, year = 2005 } @misc{citeulike:484851, abstract = {We describe online collaborative communities by tripartite networks, the nodes being persons, items and tags. We introduce projection methods in order to uncover the structures of the networks, i.e. communities of users, genre families...
To do so, we focus on the correlations between the nodes, depending on their profiles, and use percolation techniques that consist in removing less correlated links and observing the shaping of disconnected islands. The structuring of the network is visualised by using a tree representation. The notion of diversity in the system is also discussed.}, author = {Lambiotte, R. and Ausloos, M.}, citeulike-article-id = {484851}, comment = {Paper about the three parts USERS, RESOURCES and TAGS.}, eprint = {cs.DS/0512090}, interhash = {7a9dab1c733e8e1982d5f91979749ce9}, intrahash = {65c6f348a54f872fb3e60b4bd64b485b}, month = dec, note = {{\tt arXiv:cs.DS/0512090}}, priority = {3}, title = {Collaborative tagging as a tripartite network}, url = {http://arxiv.org/abs/cs.DS/0512090}, year = 2005 } @article{hereth03conceptual, abstract = {In this paper we discuss Conceptual Knowledge Discovery in Databases (CKDD) as it is developing in the field of Conceptual Knowledge Processing. Conceptual Knowledge Processing is based on the mathematical theory of Formal Concept Analysis which has become a successful theory for data analysis during the last two decades. CKDD aims to support a human-centered process of discovering knowledge from data by visualizing and analyzing the conceptual structure of the data. We dicuss how the management system TOSCANA for conceptual information systems supports CKDD, and illustrate it by two applications in database marketing and flight movement analysis. Finally, we present a new tool for conceptual deviation discovery, Chianti.}, author = {Hereth, Joachim and Stumme, Gerd and Wille, Rudolf and Wille, Uta}, comment = {alpha}, interhash = {a9c05101aeb799232425d7651a581684}, intrahash = {edffeb9bd2aaac559f2a6233dd49ae3b}, journal = {Journal of Applied Artificial Intelligence (AAI)}, number = 3, pages = {281-301}, title = {Conceptual Knowledge Discovery - a Human-Centered Approach}, url = {http://www.kde.cs.uni-kassel.de/stumme/papers/2003/hereth2003conceptual.pdf}, volume = 17, year = 2003 } @techreport{stumme99conceptualknowledge, author = {Stumme, G.}, comment = {alpha}, institution = {TU Darmstadt}, interhash = {c33970150f97bad7972281e38b42738f}, intrahash = {6d562dc043ba698acee8a83ce35bde6e}, title = {Conceptual Knowledge Discovery with Frequent Concept Lattices}, type = {{FB}4-{P}reprint 2043}, url = {http://www.kde.cs.uni-kassel.de/stumme/papers/1999/P2043.pdf}, year = 1999 } @inproceedings{bastide00levelwise, address = {France}, author = {Bastide, Y. and Taouil, R. and Pasquier, N. and Stumme, G. and Lakhal, L.}, booktitle = {Actes des 16ièmes Journées Bases de Données Avancées}, comment = {alpha}, interhash = {a3181cc73b190099592107cf465c4e43}, intrahash = {a11de6a74851c7076452159d1b12489b}, month = {Oct 24-27}, pages = {307-322}, publisher = {Blois}, title = {Levelwise Search of Frequent Patterns}, url = {http://www.kde.cs.uni-kassel.de/stumme/papers/2000/BDA00.pdf}, year = 2000 } @article{pasquier2005generating, author = {Pasquier, Nicolas and Taouil, Rafik and Bastide, Yves and Stumme, Gerd and Lakhal, Lotfi}, comment = {alpha}, interhash = {cb0ee99fae39f2a5e0af5be9d97978f5}, intrahash = {40f59a7fa7ce5015f9ee81709db89de0}, journal = {Journal Intelligent Information Systems (JIIS)}, number = 1, pages = {29-60}, publisher = {Kluwer Academic Publishers}, title = {Generating a Condensed Representation for Association Rules}, url = {http://www.kde.cs.uni-kassel.de/stumme/papers/2005/pasquier2005generating.pdf}, volume = 24, year = 2005 } @inproceedings{stumme98conceptual, address = {Heidelberg}, author = {Stumme, Gerd and Wille, Rudolf and Wille, Uta}, booktitle = {Principles of Data Mining and Knowledge Discovery Proc. 2nd European Symposium on PKDD'98}, editor = {Zytkow, J. M. and Quafofou, M.}, interhash = {5ef89b6f8fb22f9d24eda7da71b8bdb1}, intrahash = {b960d1d34dace39052a0530ab4026e18}, note = {{P}art of \cite{hereth03conceptual}}, page = {318-331}, pages = {450-458}, series = {LNAI}, title = {Conceptual Knowledge Discovery in Databases Using Formal Concept Analysis Methods}, url = {http://www.kde.cs.uni-kassel.de/stumme/papers/1998/P1993-PKDD98.pdf}, volume = 1510, year = 1998 } @article{stumme03off, abstract = {In the last years, the main orientation of Formal Concept Analysis (FCA) has turned from mathematics towards computer science. This article provides a review of this new orientation and analyzes why and how FCA and computer science attracted each other. It discusses FCA as a knowledge representation formalism using five knowledge representation principles provided by Davis, Shrobe, and Szolovits (1993). It then studies how and why mathematics-based researchers got attracted by computer science. We will argue for continuing this trend by integrating the two research areas FCA and Ontology Engineering. The second part of the article discusses three lines of research which witness the new orientation of Formal Concept Analysis: FCA as a conceptual clustering technique and its application for supporting the merging of ontologies; the efficient computation of association rules and the structuring of the results; and the visualization and management of conceptual hierarchies and ontologies including its application in an email management system.}, author = {Stumme, G.}, comment = {alpha}, interhash = {230f52a01e0807b91e2c36fb5610b1c6}, intrahash = {3ad5183ad5e15d93898a798bd5063194}, journal = {Intl. J. Human-Comuter Studies (IJHCS)}, month = {September}, number = 3, pages = {287-325}, title = {Off to New Shores -- Conceptual Knowledge Discovery and Processing}, url = {http://www.kde.cs.uni-kassel.de/stumme/papers/2003/stumme2003off.pdf}, volume = 59, year = 2003 } @inbook{lakhal2005efficient, abstract = {Association rules are a popular knowledge discovery technique for warehouse basket analysis. They indicate which items of the warehouse are frequently bought together. The problem of association rule mining has first been stated in 1993. Five years later, several research groups discovered that this problem has a strong connection to Formal Concept Analysis (FCA). In this survey, we will first introduce some basic ideas of this connection along a specific algorithm, \titanic, and show how FCA helps in reducing the number of resulting rules without loss of information, before giving a general overview over the history and state of the art of applying FCA for association rule mining.}, address = {Heidelberg}, author = {Lakhal, Lotfi and Stumme, Gerd}, booktitle = {Formal Concept Analysis: Foundations and Applications}, editor = {Ganter, Bernhard and Stumme, Gerd and Wille, Rudolf}, ee = {http://dx.doi.org/10.1007/11528784_10}, interhash = {f5777a0f9dccfcf4f9968119d77297fc}, intrahash = {2b350f817428e4c6c7259cd279815091}, pages = {180-195}, publisher = {Springer}, series = {LNAI}, title = {Efficient Mining of Association Rules Based on Formal Concept Analysis}, url = {http://www.kde.cs.uni-kassel.de/stumme/papers/2005/lakhal2005efficient.pdf}, volume = 3626, year = 2005 } @inproceedings{stumme02efficient, address = {Heidelberg}, author = {Stumme, G.}, booktitle = {Database and Expert Systems Applications. Proc. DEXA 2002}, editor = {Hameurlain, A. and Cicchetti, R. and Traunmüller, R.}, interhash = {56611a15d60e2711a0aafc257715c03d}, intrahash = {0adce6a0db24566bb55d6e2d6667c8e7}, pages = {534-546}, publisher = {Springer}, series = {LNCS}, title = {Efficient Data Mining Based on Formal Concept Analysis}, url = {http://www.kde.cs.uni-kassel.de/stumme/papers/2002/DEXA02.pdf}, volume = 2453, year = 2002 } @inproceedings{stumme01conceptualclustering, address = {Universität Dortmund 763}, author = {Stumme, G. and Taouil, R. and Bastide, Y. and Lakhal, L.}, booktitle = {Proc. GI-Fachgruppentreffen Maschinelles Lernen (FGML'01)}, editor = {Klinkenberg, R. and Rüping, S. and Fick, A. and Henze, N. and Herzog, C. and Molitor, R. and Schröder, O.}, interhash = {c99f2ae002435208c58f9244d298a10b}, intrahash = {f4ec21d5f63dbc213a3a6eae076c4b62}, month = {October}, title = {Conceptual Clustering with Iceberg Concept Lattices}, url = {http://www.kde.cs.uni-kassel.de/stumme/papers/2001/FGML01.pdf}, year = 2001 } @inproceedings{bastide00miningminimal, address = {Heidelberg}, author = {Bastide, Y. and Pasquier, N. and Taouil, R. and Stumme, G. and Lakhal, L.}, booktitle = {Computational Logic --- CL 2000 Proc. CL'00}, editor = {Lloyd, J. and Dahl, V. and Furbach, U. and Kerber, M. and Laus, K.-K. and Palamidessi, C. and Pereira, L.M. and Sagiv, Y. and Stuckey, P.J.}, interhash = {dc10d0ad3c40463f049ac775cb250f3d}, intrahash = {25fa3431e7bdd7057f9b80a7385cd718}, page = {972-986}, publisher = {Springer}, series = {LNAI}, title = {Mining Minimal Non-Redundant Association Rules Using Frequent Closed Itemsets}, url = {http://www.kde.cs.uni-kassel.de/stumme/papers/2000/DOOD00.pdf}, volume = 1861, year = 2000 } @article{bastide00miningfrequent, author = {Bastide, Y. and Taouil, R. and Pasquier, N. and Stumme, G. and Lakhal, L.}, interhash = {86c9586115ef1c7ec8539257849d9842}, intrahash = {1e79f8ba76044d2c78cea441eeba84aa}, journal = {SIGKDD Explorations, Special Issue on Scalable Algorithms}, number = 2, pages = {71-80}, title = {Mining Frequent Patterns with Counting Inference.}, volume = 2, year = 2000 } @inproceedings{hereth2000conceptual, address = {Heidelberg}, author = {Hereth, J. and Stumme, G. and Wille, R. and Wille, U.}, booktitle = {Conceptual Structures: Logical, Linguistic, and Computational Issues. Proc. ICCS '00}, editor = {Ganter, B. and Mineau, G. W.}, interhash = {8a4c0c21d83c25bb78f80e89dd36a89a}, intrahash = {577e0f2074bbece17498848014d14705}, page = {{P}art of \cite{hereth03conceptual}}, pages = {421-437}, publisher = {Springer}, series = {LNAI}, title = {Conceptual Knowledge Discovery and Data Analysis}, url = {http://www.kde.cs.uni-kassel.de/stumme/papers/2000/P2092_ICCS00_kdd.pdf}, volume = 1867, year = 2000 }