@article{keyhere, abstract = {Formal Concept Analysis, developed during the last fifteen years, has been based on the dyadic understanding of a concept constituted by its extension and its intension. The pragmatic philosophy of Charles S. Peirce with his three universal categories, and experiences in data analysis, have suggested a triadic approach to Formal Concept Analysis. This approach starts with the primitive notion of a triadic context defined as a quadruple (G, M, B, Y) where G, M, and B are sets and Y is a ternary relation between G, M, and B, i.e. Y G×M×B; the elements of G, M, and B are called objects, attributes, and conditions, respectively, and (g, m,b) Y is read: the object g has the attribute m under (or according to) the condition b. A triadic concept of a triadic context (G, M, B, Y) is defined as a triple (A}, author = {Lehmann, Fritz and Wille, Rudolf}, interhash = {fa3e423ee0c5a9dadc3211c3f8a5e4a6}, intrahash = {2d18c634917546cfa78a73901f0963a3}, journal = {Conceptual Structures: Applications, Implementation and Theory}, pages = {32--43}, title = {A triadic approach to formal concept analysis}, url = {http://dx.doi.org/10.1007/3-540-60161-9_27}, year = 1995 } @inproceedings{IfrimTW-ICML2005, address = {Bonn, Germany}, author = {Ifrim, Georgiana and Theobald, Martin and Weikum, Gerhard}, booktitle = {Proceedings of the 22nd International Conference on Machine Learning - Learning in Web Search (LWS 2005)}, editor = {Raedt, Luc De and Wrobel, Stefan}, interhash = {a54c4070e0fb55f5a084a0f088230a65}, intrahash = {57f8241941ed979455c3dbb90893020f}, isbn = {1-59593-180-5}, pages = {18--26}, title = {Learning Word-to-Concept Mappings for Automatic Text Classification}, url = {http://www.mpi-inf.mpg.de/~ifrim/publications/icml-lws05.pdf}, year = 2005 } @article{jaeschke2008discovering, abstract = {Social bookmarking tools are rapidly emerging on the Web. In such systems users are setting up lightweight conceptual structures called folksonomies. Unlike ontologies, shared conceptualizations are not formalized, but rather implicit. We present a new data mining task, the mining of all frequent tri-concepts, together with an efficient algorithm, for discovering these implicit shared conceptualizations. Our approach extends the data mining task of discovering all closed itemsets to three-dimensional data structures to allow for mining folksonomies. We provide a formal definition of the problem, and present an efficient algorithm for its solution. Finally, we show the applicability of our approach on three large real-world examples.}, author = {Jäschke, Robert and Hotho, Andreas and Schmitz, Christoph and Ganter, Bernhard and Stumme, Gerd}, booktitle = {Semantic Web and Web 2.0}, interhash = {cfca594f9dbe30694bfbcdeb40dc4e88}, intrahash = {63901930c137df0c2dad84075c564b14}, journal = {Web Semantics: Science, Services and Agents on the World Wide Web}, month = feb, number = 1, pages = {38--53}, title = {Discovering Shared Conceptualizations in Folksonomies}, url = {http://www.sciencedirect.com/science/article/B758F-4R53WD4-1/2/ae56bd6e7132074272ca2035be13781b}, volume = 6, year = 2008 } @inproceedings{sanderson99-deriving, author = {Sanderson, Mark and Croft, William Bruce}, booktitle = {Proceedings of the 22nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR'99}, interhash = {b351eb1a827b4d024323c4706035c938}, intrahash = {d15caaaea82b6df0747cc298a8b13556}, lastdatemodified = {2007-04-14}, lastname = {Sanderson}, own = {notown}, pages = {206--213}, pdf = {sanderson99-deriving.pdf}, read = {notread}, title = {Deriving concept hierarchies from text}, year = 1999 } @article{fischer87, abstract = {Conceptual clustering is an important way of summarizing and explaining data. However, the recent formulation of this paradigm has allowed little exploration of conceptual clustering as a means of improving performance. Furthermore, previous work in conceptual clustering has not explicitly dealt with constraints imposed by real world environments. This article presents COBWEB, a conceptual clustering system that organizes data so as to maximize inference ability. Additionally, COBWEB is incremental and computationally economical, and thus can be flexibly applied in a variety of domains.}, author = {Fisher, Douglas H.}, interhash = {36208ac57cc67951de85bd99b8fb8647}, intrahash = {0edbe48f91025efea4af0a1a62433e42}, journal = {Machine Learning}, month = {September}, number = 2, pages = {139--172}, title = {Knowledge Acquisition Via Incremental Conceptual Clustering}, volume = 2, year = 1987 } @book{HanKamber01, address = {San Francisco, LA}, author = {Han and Kamber}, interhash = {f902c324cdc1b270bdf9d996ba19dca7}, intrahash = {b9884cf23229d6cb71535484424be1ff}, location = {Helsinki, Finland}, publisher = {Morgan Kaufmann}, title = {Data Mining. Concepts and Techniques}, year = 2001 } @inproceedings{MobasheretalCHI2001, address = {New Orleans, LA}, author = {Parent, S. and Mobasher, B. and Lytinen, S.}, booktitle = {Proceedings of the 9th International Conference on Human Computer Interaction}, interhash = {9539ed5408f62ddb8d5c790fabac2e76}, intrahash = {aaf8f9dccc3d89097704896af6b78536}, title = {An Adaptive Agent for Web Exploration Based of Concept Hierarchies}, url = {citeseer.nj.nec.com/443168.html}, url2 = {www.cs.umn.edu/research/websift/papers/rwc\_thesis.ps}, year = 2001 } @book{GW99, address = {Berlin -- Heidelberg}, author = {Ganter, B. and Wille, R.}, interhash = {1b0bf49069eadcdfac42e52addf4eb9d}, intrahash = {ee411290ea5b80d257ac115b2738237c}, location = {Santa Barbara, CA}, publisher = {Springer}, title = {Formal Concept Analysis: Mathematical Foundations}, year = 1999 } @article{STBPL01, author = {Stumme, G. and Taouil, R. and Bastide, Y. and Pasqier, N. and Lakhal, L.}, bb-further-address = {--Dordrecht--London}, interhash = {5d7f2955cda84c348e5224c929829823}, intrahash = {54a98970c6aad7936e6f246b3cc414a7}, journal = {J. on Knowledge and Data Engineering}, number = 2, pages = {189--222}, title = {Computing Iceberg Concept Lattices with Titanic}, volume = 42, year = 2002 }