http://www.bibsonomy.org/burst/user/hotho/rulesBibSonomy BuRST Feed for /user/hotho/rules2008-10-07T18:42:04+02:00http://www.bibsonomy.org/bibtex/242b4c94cff05ccef031235d661a7a77a/hothohotho2008-05-09T12:06:13+02:00detection association rules community folksonomy del.icio.us *** clustering <span style="color:#555555;">Xin <a href="http://www.bibsonomy.org/author/Li">Li</a> und Lei <a href="http://www.bibsonomy.org/author/Guo">Guo</a> und 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>Seite675-684. </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 Discovery2008detection association rules community folksonomy del.icio.us *** clustering 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.http://www.bibsonomy.org/bibtex/2c8dbb6371be8d67e3aa1928bd3dd0fed/hothohotho2008-01-15T10:57:12+01:002006 sosbuch seminar2006 myown rules association folksonomy analysis network summerschool semantic kdubiq <span style="color:#555555;">Christoph <a href="http://www.bibsonomy.org/author/Schmitz">Schmitz</a> und Andreas <a href="http://www.bibsonomy.org/author/Hotho">Hotho</a> und Robert <a href="http://www.bibsonomy.org/author/Jäschke">J&#228;schke</a> und Gerd <a href="http://www.bibsonomy.org/author/Stumme">Stumme</a> </span><em>Data Science and Classification (Proc. IFCS 2006 Conference), </em><em>Seite261-270. </em><em>Ljubljana, </em><em>Springer, </em><em>July2006. </em>Tue Jan 15 10:57:12 CET 2008LjubljanaData Science and Classification (Proc. IFCS 2006 Conference)July261-270Studies in Classification, Data Analysis, and Knowledge OrganizationMining Association Rules in Folksonomies20062006 sosbuch seminar2006 myown rules association folksonomy analysis network summerschool semantic kdubiq dblphttp://www.bibsonomy.org/bibtex/272825e6a12b3285349fb64c1020383c0/hothohotho2006-05-30T08:02:34+02:00lecture mining rules association kdd constraints 2006 <span style="color:#555555;">Raymond T. <a href="http://www.bibsonomy.org/author/Ng">Ng</a> und Laks V. S. <a href="http://www.bibsonomy.org/author/Lakshmanan">Lakshmanan</a> und Jiawei <a href="http://www.bibsonomy.org/author/Han">Han</a> und Alex <a href="http://www.bibsonomy.org/author/Pang">Pang</a> </span><em>SIGMOD Conference, </em><em>Seite13-24. </em>(<em>1998</em>)Tue May 30 08:02:34 CEST 2006SIGMOD Conference13-24Exploratory Mining and Pruning Optimizations of Constrained Association Rules.1998lecture mining rules association kdd constraints 2006 http://www.bibsonomy.org/bibtex/2094af08c931c876e20fd0e1e5086583b/hothohotho2006-05-30T07:46:42+02:00hash association kdd rules lecture table mining 2006 <span style="color:#555555;">J.S. <a href="http://www.bibsonomy.org/author/Park">Park</a> und M.S. <a href="http://www.bibsonomy.org/author/Chen">Chen</a> und P.S. <a href="http://www.bibsonomy.org/author/Yu">Yu</a> </span><em>Proceedings of the 1995 ACM SIGMOD international conference on Management of data</em>(<em>1995</em>)Tue May 30 07:46:42 CEST 2006Proceedings of the 1995 ACM SIGMOD international conference on Management of data175-186{An effective hash-based algorithm for mining association rules}1995hash association kdd rules lecture table mining 2006 http://www.bibsonomy.org/bibtex/28c68af52691fe5b3f4db26dea63c8a31/hothohotho2005-12-20T20:21:42+01:00mining rules generalized association <span style="color:#555555;">R. <a href="http://www.bibsonomy.org/author/Srikant">Srikant</a> und R. <a href="http://www.bibsonomy.org/author/Agrawal">Agrawal</a> </span><em>Proceedings of the 21st International Conference on Very Large Databases, </em><em>Seite407--419. </em><em>Sep1995. </em>Tue Dec 20 20:21:42 CET 2005Proceedings of the 21st International Conference on Very Large DatabasesSep407--419Mining Generalized Association Rules1995mining rules generalized association