http://www.bibsonomy.org/burst/concept/user/hotho/dmBibSonomy BuRST Feed for /concept/user/hotho/dm2008-07-27T03:48:51+02:00D5 MPI-INF Publications: Proceedings Article: Learning Word-to-Concept Mappings for Automatic Text Classificationhttp://www.bibsonomy.org/bibtex/257f8241941ed979455c3dbb90893020f/hothohotho2008-07-01T15:19:39+02:00tc text concept topic classification model wordnet <span style="color:#555555;">Georgiana <a href="http://www.bibsonomy.org/author/Ifrim">Ifrim</a> and Martin <a href="http://www.bibsonomy.org/author/Theobald">Theobald</a> and Gerhard <a href="http://www.bibsonomy.org/author/Weikum">Weikum</a> </span><em>Proceedings of the 22nd International Conference on Machine Learning - Learning in Web Search (LWS 2005), </em><em>page18--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 Classification2005tc text concept topic classification model wordnet Institut AIFB - Publikation: AEON - An Approach to the Automatic Evaluation of Ontologieshttp://www.bibsonomy.org/bibtex/2ea55fe7088ef25cdf060d30d94a09e26/hothohotho2008-06-17T22:30:50+02:00myown 2008 automatic ontology evaluation sw ml <span style="color:#555555;">Johanna <a href="http://www.bibsonomy.org/author/Völker">V&#246;lker</a> and Denny <a href="http://www.bibsonomy.org/author/Vrandecic">Vrandecic</a> and York <a href="http://www.bibsonomy.org/author/Sure">Sure</a> and Andreas <a href="http://www.bibsonomy.org/author/Hotho">Hotho</a> </span><em>Journal of Applied Ontology</em>(<em>2008</em>) <em>to appear . </em>Tue Jun 17 22:30:50 CEST 2008Journal of Applied Ontologyto appearAEON - An Approach to the Automatic Evaluation of Ontologies2008myown 2008 automatic ontology evaluation sw ml Multi-way distributional clustering via pairwise interactionshttp://www.bibsonomy.org/bibtex/2a5ac489feb7407a07570f6733665a6dd/hothohotho2008-06-04T11:45:49+02:00coclustering clustering graph multiway multi hierarchical <span style="color:#555555;">Ron <a href="http://www.bibsonomy.org/author/Bekkerman">Bekkerman</a> and Ran <a href="http://www.bibsonomy.org/author/El-Yaniv">El-Yaniv</a> and Andrew <a href="http://www.bibsonomy.org/author/McCallum">McCallum</a> </span><em>ICML '05: Proceedings of the 22nd international conference on Machine learning, </em><em>page41--48. </em><em>New York, NY, USA, </em><em>ACM Press, </em>(<em>2005</em>)Wed Jun 04 11:45:49 CEST 2008New York, NY, USAICML '05: Proceedings of the 22nd international conference on Machine learning41--48Multi-way distributional clustering via pairwise interactions2005coclustering clustering graph multiway multi hierarchical http://www.bibsonomy.org/bibtex/256de7e6d214faebdbf2f2ef0fce09d7d/hothohotho2008-06-02T15:12:04+02:00algorithm networks fast community clustering <span style="color:#555555;">M.E.J. <a href="http://www.bibsonomy.org/author/Newman">Newman</a> </span><em>Physical Review E</em><em>September2003. </em>Mon Jun 02 15:12:04 CEST 2008Physical Review ESeptemberFast algorithm for detecting community structure in networks692003algorithm networks fast community clustering http://www.bibsonomy.org/bibtex/242b4c94cff05ccef031235d661a7a77a/hothohotho2008-05-09T12:06:13+02:00clustering del.icio.us *** folksonomy detection association rules community <span style="color:#555555;">Xin <a href="http://www.bibsonomy.org/author/Li">Li</a> and Lei <a href="http://www.bibsonomy.org/author/Guo">Guo</a> and Yihong E. <a href="http://www.bibsonomy.org/author/Zhao">Zhao</a> </span><em>Proceedings of the 17th International World Wide Web Conference, </em><em>page675-684. </em><em>ACM, </em>(<em>2008</em>)Fri May 09 12:06:13 CEST 2008Proceedings of the 17th International World Wide Web Conference675-684Tag-based Social Interest Discovery2008clustering del.icio.us *** folksonomy detection association rules community The success and popularity of social network systems, such as del.icio.us, Facebook, MySpace, and YouTube, have generated many interesting and challenging problems to the research community. Among others, discovering social interests shared by groups of users is very important because it helps to connect people with common interests and encourages people to contribute and share more contents. The main challenge to solving this problem comes from the diffi- culty of detecting and representing the interest of the users. The existing approaches are all based on the online connections of users and so unable to identify the common interest of users who have no online connections. In this paper, we propose a novel social interest discovery approach based on user-generated tags. Our approach is motivated by the key observation that in a social network, human users tend to use descriptive tags to annotate the contents that they are interested in. Our analysis on a large amount of real-world traces reveals that in general, user-generated tags are consistent with the web content they are attached to, while more concise and closer to the understanding and judgments of human users about the content. Thus, patterns of frequent co-occurrences of user tags can be used to characterize and capture topics of user interests. We have developed an Internet Social Interest Discovery system, ISID, to discover the common user interests and cluster users and their saved URLs by different interest topics. Our evaluation shows that ISID can effectively cluster similar documents by interest topics and discover user communities with common interests no matter if they have any online connections.BibSonomy::edit bibtexhttp://www.bibsonomy.org/bibtex/276d1018ba398695e454d20de302de6e6/hothohotho2008-05-06T09:43:15+02:00toread clustering code SVM <span style="color:#555555;">Sei-Hyung <a href="http://www.bibsonomy.org/author/Lee">Lee</a> and Karen M. <a href="http://www.bibsonomy.org/author/Daniels">Daniels</a> </span><em>Proceedings of 6th SIAM Conference on Data Mining, </em><em>page484&#8211;488. </em><em>May2006. </em>Tue May 06 09:43:15 CEST 2008Proceedings of 6th SIAM Conference on Data MiningMay484–488Cone Cluster Labeling for Support Vector Clustering2006toread clustering code SVM A framework for community identification in dynamic social networkshttp://www.bibsonomy.org/bibtex/227a4fb58300979d4dbe94e75422418bd/hothohotho2008-04-26T12:29:32+02:00clustering graph community toread detection <span style="color:#555555;">Chayant <a href="http://www.bibsonomy.org/author/Tantipathananandh">Tantipathananandh</a> and Tanya <a href="http://www.bibsonomy.org/author/Berger-Wolf">Berger-Wolf</a> and David <a href="http://www.bibsonomy.org/author/Kempe">Kempe</a> </span><em>KDD '07: Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining, </em><em>page717--726. </em><em>New York, NY, USA, </em><em>ACM, </em>(<em>2007</em>)Sat Apr 26 12:29:32 CEST 2008New York, NY, USAKDD '07: Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining717--726A framework for community identification in dynamic social networks2007clustering graph community toread detection http://www.bibsonomy.org/bibtex/203d349d70b578ca9ac3155f661151868/hothohotho2008-04-23T13:56:47+02:00ml myown mining folksonomy 2008 social dm bookmarking spam classification <span style="color:#555555;">Beate <a href="http://www.bibsonomy.org/author/Krause">Krause</a> and Andreas <a href="http://www.bibsonomy.org/author/Hotho">Hotho</a> and Gerd <a href="http://www.bibsonomy.org/author/Stumme">Stumme</a> </span><em>Proc. of the Fourth International Workshop on Adversarial Information Retrieval on the Web, </em>(<em>2008</em>)Wed Apr 23 13:56:47 CEST 2008Proc. of the Fourth International Workshop on Adversarial Information Retrieval on the WebThe Anti-Social Tagger - Detecting Spam in Social Bookmarking Systems2008ml myown mining folksonomy 2008 social dm bookmarking spam classification Amazon.de: Text Mining. Predictive Methods for Analyzing Unstructured Information: Sholom M. Weiss,Nitin Indurkhya,T. Zhang: English Bookshttp://www.bibsonomy.org/bibtex/26ac07561b543e6033fd4c9811d0dccad/hothohotho2008-04-12T15:25:20+02:00dm tm mining software text nlp <span style="color:#555555;">Sholom M. <a href="http://www.bibsonomy.org/author/Weiss">Weiss</a> and Nitin <a href="http://www.bibsonomy.org/author/Indurkhya">Indurkhya</a> and T. <a href="http://www.bibsonomy.org/author/Zhang">Zhang</a> </span><em>Springer, Berlin, </em><em>1 edition, </em>(<em>2004</em>)Sat Apr 12 15:25:20 CEST 20081Text Mining. Predictive Methods for Analyzing Unstructured Information2004dm tm mining software text nlp DBLP Record 'conf/iis/OsinskiSW04'http://www.bibsonomy.org/bibtex/240aba631c1ac8819bd64b0ee74bfdd1b/hothohotho2008-03-21T15:24:47+01:00lsi clustering toread svd <span style="color:#555555;">Stanislaw <a href="http://www.bibsonomy.org/author/Osinski">Osinski</a> and Jerzy <a href="http://www.bibsonomy.org/author/Stefanowski">Stefanowski</a> and Dawid <a href="http://www.bibsonomy.org/author/Weiss">Weiss</a> </span><em>Intelligent Information Systems, </em><em>page359-368. </em>(<em>2004</em>)Fri Mar 21 15:24:47 CET 2008Intelligent Information SystemsConfIis2004359-368Lingo: Search Results Clustering Algorithm Based on Singular Value Decomposition2004lsi clustering toread svd SpringerLink - Book Chapterhttp://www.bibsonomy.org/bibtex/2121bdc75ba2f61db089bac5f715a07ba/hothohotho2008-02-28T10:16:38+01:00myown text ol classification clustering 2006 <span style="color:#555555;">Stephan <a href="http://www.bibsonomy.org/author/Bloehdorn">Bloehdorn</a> and Philipp <a href="http://www.bibsonomy.org/author/Cimiano">Cimiano</a> and Andreas <a href="http://www.bibsonomy.org/author/Hotho">Hotho</a> </span><em>From Data and Information Analysis to Knowledge Engineering</em>(<em>2006</em>)Thu Feb 28 10:16:38 CET 2008From Data and Information Analysis to Knowledge Engineering334--341Learning Ontologies to Improve Text Clustering and Classification2006myown text ol classification clustering 2006 Recent work has shown improvements in text clustering and classification tasks by integrating conceptual features extracted from ontologies. In this paper we present text mining experiments in the medical domain in which the ontological structures used are acquired automatically in an unsupervised learning process from the text corpus in question. We compare results obtained using the automatically learned ontologies with those obtained using manually engineered ones. Our results show that both types of ontologies improve results on text clustering and classification tasks, whereby the automatically acquired ontologies yield a improvement competitive with the manually engineered ones. ER -Approximating the Community Structure of the Long Tailhttp://www.bibsonomy.org/bibtex/2386f36679c111f30e37ced272d5b355c/hothohotho2008-02-19T10:24:54+01:00detection svd community toread clustering <span style="color:#555555;">Akshay <a href="http://www.bibsonomy.org/author/Java">Java</a> and Anupam <a href="http://www.bibsonomy.org/author/Joshi">Joshi</a> and Tim <a href="http://www.bibsonomy.org/author/FininBook">FininBook</a> </span><em>Proceedings of the Second International Conference on Weblogs and Social Media(ICWSM 2008), </em><em>AAAI Press, </em>(<em>2008</em>)Tue Feb 19 10:24:54 CET 2008Proceedings of the Second International Conference on Weblogs and Social Media(ICWSM 2008)Approximating the Community Structure of the Long Tail2008detection svd community toread clustering In many social media applications, a small fraction of the members are highly linked while most are sparsely connected to the network. Such a skewed distribution is sometimes referred to as the"long tail". Popular applications like meme trackers and content aggregators mine for information from only the popular blogs located at the head of this curve. On the other hand, the long tail contains large volumes of interesting information and niches. The question we address in this work is how best to approximate the community membership of entities in the long tail using only a small percentage of the entire graph structure. Our technique utilizes basic linear algebra manipulations and spectral methods. It has the advantage of quickly and efficiently finding a reasonable approximation of the community structure of the overall network. Such a method has significant applications in blog analysis engines as well as social media monitoring tools in general. http://www.bibsonomy.org/bibtex/292d3a5fdd786086fa12787e3e350b6af/hothohotho2008-02-13T12:26:23+01:00ki web ml ir myown 2007 mining introduction <span style="color:#555555;">Andreas <a href="http://www.bibsonomy.org/author/Hotho">Hotho</a> and Gerd <a href="http://www.bibsonomy.org/author/Stumme">Stumme</a> </span><em>K&#252;nstliche Intelligenz</em>(<em>2007</em>)Wed Feb 13 12:26:23 CET 2008Künstliche Intelligenz35-8Mining the World Wide Web2007ki web ml ir myown 2007 mining introduction http://www.bibsonomy.org/bibtex/2e472dc4e61921ed15175756fcd9fea6a/hothohotho2008-02-02T10:31:29+01:00clustering classification text <span style="color:#555555;">L. <a href="http://www.bibsonomy.org/author/Douglas Baker">Douglas Baker</a> and Andrew K. <a href="http://www.bibsonomy.org/author/McCallum">McCallum</a> </span><em>Proceedings of SIGIR-98, 21st ACM International Conference on Research and Development in Information Retrieval, </em><em>page96--103. </em><em>Melbourne, AU, </em><em>ACM Press, New York, US, </em>(<em>1998</em>)Sat Feb 02 10:31:29 CET 2008Melbourne, AUProceedings of {SIGIR}-98, 21st {ACM} International Conference on Research and Development in Information Retrieval96--103Distributional clustering of words for text classification1998clustering classification text http://www.bibsonomy.org/bibtex/2334d3ab11400c4a3ea3ed5b1e95c1855/hothohotho2008-02-01T09:28:11+01:00tagging social clustering summerschool bookmarking kdubiq conceptual folksonomy 2007 myown sosbuch <span style="color:#555555;">Miranda <a href="http://www.bibsonomy.org/author/Grahl">Grahl</a> and Andreas <a href="http://www.bibsonomy.org/author/Hotho">Hotho</a> and Gerd <a href="http://www.bibsonomy.org/author/Stumme">Stumme</a> </span><em>7th International Conference on Knowledge Management (I-KNOW '07), </em><em>page356-364. </em><em>Graz, Austria, </em><em>Know-Center, </em><em>SEP2007. </em>Fri Feb 01 09:28:11 CET 2008Graz, Austria7th International Conference on Knowledge Management (I-KNOW '07)SEP356-364Conceptual Clustering of Social Bookmarking Sites2007tagging social clustering summerschool bookmarking kdubiq conceptual folksonomy 2007 myown sosbuch Currently, social bookmarking systems provide intuitive support for browsing locally their content. A global view is usually presented by the tag cloud of the system, but it does not allow a conceptual drill-down, e. g., along a conceptual hierarchy. In this paper, we present a clustering approach for computing such a conceptual hierarchy for a given folksonomy. The hierarchy is complemented with ranked lists of users and resources most related to each cluster. The rankings are computed using our FolkRank algorithm. We have evaluated our approach on large scale data from the del.icio.us bookmarking system.http://www.bibsonomy.org/bibtex/26d5188d66564fe4ed7386e28868504de/hothohotho2008-01-31T12:22:52+01:00collaborative social folksonomy clustering myown 2007 bookmarking <span style="color:#555555;">Miranda <a href="http://www.bibsonomy.org/author/Grahl">Grahl</a> and Andreas <a href="http://www.bibsonomy.org/author/Hotho">Hotho</a> and Gerd <a href="http://www.bibsonomy.org/author/Stumme">Stumme</a> </span><em>Workshop Proceedings of Lernen - Wissensentdeckung - Adaptivit&#228;t (LWA 2007), </em><em>page50-54. </em><em>Martin-Luther-Universit&#228;t Halle-Wittenberg, </em><em>sep2007. </em>Thu Jan 31 12:22:52 CET 2008Workshop Proceedings of Lernen - Wissensentdeckung - Adaptivität (LWA 2007)sep50-54Conceptual Clustering of Social Bookmark Sites2007collaborative social folksonomy clustering myown 2007 bookmarking From Web to Social Web: Discovering and Deploying User and Cont... - Data Mi...Journals, Books & Online Media | Springerhttp://www.bibsonomy.org/bibtex/28aa8d9bcbb5a5bb3fc480d1e53b27236/hothohotho2008-01-31T12:01:31+01:00dm data myown tm mining 2007 web social <span style="color:#555555;">B. <a href="http://www.bibsonomy.org/author/Berendt">Berendt</a> and A. <a href="http://www.bibsonomy.org/author/Hotho">Hotho</a> and D. <a href="http://www.bibsonomy.org/author/Mladenic">Mladenic</a> and G. <a href="http://www.bibsonomy.org/author/Semeraro">Semeraro</a> (eds.). </span><em>LNCS </em><em>Springer, </em>(<em>2007</em>)Thu Jan 31 12:01:31 CET 2008LNCSFrom Web to Social Web: Discovering and Deploying User and Content Profiles 47362007dm data myown tm mining 2007 web social This book constitutes the refereed proceedings of the Workshop on Web Mining, WebMine 2006, held in Berlin, Germany, September 18th, 2006. Topics included are data mining based on analysis of bloggers and tagging, web mining, XML mining and further techniques of knowledge discovery. The book is especially valuable for those interested in the aspects of the Social Web (Web 2.0) and its inherent dynamic and diversity of user-generated content.Text Clustering Based on Good Aggregationshttp://www.bibsonomy.org/bibtex/2a6803e87c5145d5f55d7bb1bab8dfd67/hothohotho2008-01-28T15:00:01+01:00myown ontology clustering tm gruppenbildung text kmeans 2001 <span style="color:#555555;">Andreas <a href="http://www.bibsonomy.org/author/Hotho">Hotho</a> and Alexander <a href="http://www.bibsonomy.org/author/Maedche">Maedche</a> and Steffen <a href="http://www.bibsonomy.org/author/Staab">Staab</a> </span><em>ICDM '01: Proceedings of the 2001 IEEE International Conference on Data Mining, </em><em>page607--608. </em><em>Washington, DC, USA, </em><em>IEEE Computer Society, </em>(<em>2001</em>)Mon Jan 28 15:00:01 CET 2008Washington, DC, USAICDM '01: Proceedings of the 2001 IEEE International Conference on Data Mining607--608Text Clustering Based on Good Aggregations2001myown ontology clustering tm gruppenbildung text kmeans 2001 Distributed feature extraction in a p2p settinghttp://www.bibsonomy.org/bibtex/2e5eba80e58b4532a3fd3bcf50994734e/hothohotho2008-01-25T20:42:37+01:00p2p kdubiq music summerschool mining dm 2.0 tagging web <span style="color:#555555;">Michael <a href="http://www.bibsonomy.org/author/Wurst">Wurst</a> and Katharina <a href="http://www.bibsonomy.org/author/Morik">Morik</a> </span><em>Future Gener. Comput. Syst.</em><em>23(1):69--75</em>(<em>2007</em>)Fri Jan 25 20:42:37 CET 2008Amsterdam, The Netherlands, The NetherlandsFuture Gener. Comput. Syst.169--75Distributed feature extraction in a p2p setting: a case study232007p2p kdubiq music summerschool mining dm 2.0 tagging web http://www.bibsonomy.org/bibtex/218fdbebb76d48feccf2dceed23f4cd74/hothohotho2008-01-15T11:04:28+01:00text clustering 2002 ontology myown <span style="color:#555555;">A. <a href="http://www.bibsonomy.org/author/Hotho">Hotho</a> and G. <a href="http://www.bibsonomy.org/author/Stumme">Stumme</a> </span><em>Proceedings of FGML Workshop, </em><em>page37-45. </em><em>Special Interest Group of German Informatics Society (FGML --- Fachgruppe Maschinelles Lernen der GI e.V.), </em>(<em>2002</em>)Tue Jan 15 11:04:28 CET 2008Proceedings of FGML Workshop37-45Conceptual Clustering of Text Clusters2002text clustering 2002 ontology myown