BibSonomyburst/user/hotho/conceptBibSonomy RSS feed for /user/hotho/concept2012-02-16T06:27:43+01:00http://www.bibsonomy.org/bibtex/22d18c634917546cfa78a73901f0963a3/hothohotho2008-10-21T18:55:23+02:00analysis concept fca formal triadic trias <span class="authorEditorList"><a href="/author/Lehmann">Fritz Lehmann</a>, and <a href="/author/Wille">Rudolf Wille</a> </span><em>Conceptual Structures: Applications, Implementation and Theory</em> (<em>1995</em>)Tue Oct 21 18:55:23 CEST 2008Conceptual Structures: Applications, Implementation and Theory32--43A triadic approach to formal concept analysis1995analysis concept fca formal triadic trias 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 (ASpringerLink - Book Chapterhttp://www.bibsonomy.org/bibtex/257f8241941ed979455c3dbb90893020f/hothohotho2008-07-01T15:19:39+02:00classification concept model tc text topic wordnet <span class="authorEditorList"><a href="/author/Ifrim">Georgiana Ifrim</a>, <a href="/author/Theobald">Martin Theobald</a>, and <a href="/author/Weikum">Gerhard Weikum</a> </span><em>Proceedings of the 22nd International Conference on Machine Learning - Learning in Web Search LWS 2005, </em><em>page 18--26. </em><em>Bonn, Germany, </em>(<em>2005</em>)Tue Jul 01 15:19:39 CEST 2008Bonn, GermanyProceedings of the 22nd International Conference on Machine Learning - Learning in Web Search (LWS 2005)18--26Learning Word-to-Concept Mappings for Automatic Text Classification2005classification concept model tc text topic wordnet D5 MPI-INF Publications: Proceedings Article: Learning Word-to-Concept Mappings for Automatic Text Classificationhttp://www.bibsonomy.org/bibtex/263901930c137df0c2dad84075c564b14/hothohotho2008-06-17T22:31:22+02:002008 analysis bibsonomy concept discovering fca folksonomy formal myown tagging taggingsurvey <span class="authorEditorList"><a href="/author/Jäschke">Robert Jäschke</a>, <a href="/author/Hotho">Andreas Hotho</a>, <a href="/author/Schmitz">Christoph Schmitz</a>, <a href="/author/Ganter">Bernhard Ganter</a>, and <a href="/author/Stumme">Gerd Stumme</a> </span><em>Web Semantics: Science, Services and Agents on the World Wide Web</em> <em>6(1):38--53</em> (<em>February 2008</em>)<em> . </em>Tue Jun 17 22:31:22 CEST 2008Semantic Web and Web 2.0Web Semantics: Science, Services and Agents on the World Wide Webfeb 138--53Discovering Shared Conceptualizations in Folksonomies620082008 analysis bibsonomy concept discovering fca folksonomy formal myown tagging taggingsurvey 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.ScienceDirect - Web Semantics: Science, Services and Agents on the World Wide Web : Discovering shared conceptualizations in folksonomieshttp://www.bibsonomy.org/bibtex/2d15caaaea82b6df0747cc298a8b13556/hothohotho2007-07-04T09:35:22+02:00clustering concept hierarchies text <span class="authorEditorList"><a href="/author/Sanderson">Mark Sanderson</a>, and <a href="/author/Croft">William Bruce Croft</a> </span><em>Proceedings of the 22nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR&#039;99, </em><em>page 206--213. </em>(<em>1999</em>)Wed Jul 04 09:35:22 CEST 2007Proceedings of the 22nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR'99206--213Deriving concept hierarchies from text1999clustering concept hierarchies text http://www.bibsonomy.org/bibtex/20edbe48f91025efea4af0a1a62433e42/hothohotho2006-03-23T12:22:43+01:00clustering climbing incremental learning hill inference concept formation conceptual <span class="authorEditorList"><a href="/author/Fisher">Douglas H. Fisher</a> </span><em>Machine Learning</em> <em>2(2):139--172</em> (<em>September 1987</em>)Thu Mar 23 12:22:43 CET 2006Machine LearningSeptember2139--172Knowledge Acquisition Via Incremental Conceptual Clustering21987clustering climbing incremental learning hill inference concept formation conceptual 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.http://www.bibsonomy.org/bibtex/2b9884cf23229d6cb71535484424be1ff/hothohotho2006-01-08T11:42:56+01:00technique data concept mining <span class="authorEditorList"><a href="/author/Han"> Han</a>, and <a href="/author/Kamber"> Kamber</a> </span><em>Morgan Kaufmann, </em><em>San Francisco, LA, </em>(<em>2001</em>)Sun Jan 08 11:42:56 CET 2006San Francisco, LAData Mining. Concepts and Techniques2001technique data concept mining http://www.bibsonomy.org/bibtex/2aaf8f9dccc3d89097704896af6b78536/hothohotho2005-12-20T20:21:42+01:00exploration web agent adaptive concept hierarchies based <span class="authorEditorList"><a href="/author/Parent">S. Parent</a>, <a href="/author/Mobasher">B. Mobasher</a>, and <a href="/author/Lytinen">S. Lytinen</a> </span><em>Proceedings of the 9th International Conference on Human Computer Interaction, </em><em>New Orleans, LA, </em>(<em>2001</em>)Tue Dec 20 20:21:42 CET 2005New Orleans, LAProceedings of the 9th International Conference on Human Computer InteractionAn Adaptive Agent for Web Exploration Based of Concept Hierarchies2001exploration web agent adaptive concept hierarchies based http://www.bibsonomy.org/bibtex/2ee411290ea5b80d257ac115b2738237c/hothohotho2005-12-20T20:21:42+01:00foundations mathematical formal concept analysis <span class="authorEditorList"><a href="/author/Ganter">B. Ganter</a>, and <a href="/author/Wille">R. Wille</a> </span><em>Springer, </em><em>Berlin -- Heidelberg, </em>(<em>1999</em>)Tue Dec 20 20:21:42 CET 2005Berlin -- HeidelbergFormal Concept Analysis: Mathematical Foundations1999foundations mathematical formal concept analysis http://www.bibsonomy.org/bibtex/254a98970c6aad7936e6f246b3cc414a7/hothohotho2005-12-20T20:21:42+01:00computing concept iceberg lattices titanic <span class="authorEditorList"><a href="/author/Stumme">G. Stumme</a>, <a href="/author/Taouil">R. Taouil</a>, <a href="/author/Bastide">Y. Bastide</a>, <a href="/author/Pasqier">N. Pasqier</a>, and <a href="/author/Lakhal">L. Lakhal</a> </span><em>J. on Knowledge and Data Engineering</em> <em>42(2):189--222</em> (<em>2002</em>)Tue Dec 20 20:21:42 CET 2005J. on Knowledge and Data Engineering2189--222Computing Iceberg Concept Lattices with Titanic422002computing concept iceberg lattices titanic