BibliographyType,ISBN,Identifier,Author,Title,Journal,Volume,Number,Month,Pages,Year,Address,Note,URL,Booktitle,Chapter,Edition,Series,Editor,Publisher,ReportType,Howpublished,Institution,Organizations,School,Annote,Custom1,Custom2,Custom3,Custom4,Custom5
6,"1-55860-153-8","672836","Agrawal, Rakesh & Srikant, Ramakrishnan","Fast Algorithms for Mining Association Rules in Large Databases","",,,"","487--499",1994,"San Francisco, CA, USA","","","VLDB '94: Proceedings of the 20th International Conference on Very Large Data Bases","","","","","Morgan Kaufmann Publishers Inc.","","","","","","","","","2006 association kdd lecture mining rule ","",""
7,"","flajolet85probabilistic","Flajolet, Philippe & Martin, G. Nigel","Probabilistic Counting Algorithms for Data Base Applications","Journal of Computer and System Sciences",31,2,"","182-209",1985,"","","http://citeseer.ist.psu.edu/flajolet85probabilistic.html","","","","","","","","","","","","","","","association counting dm kdubiq rule toread ","",""
6,"","xin2008www","Li, Xin; Guo, Lei & Zhao, Yihong E.","Tag-based Social Interest Discovery","",,,"","675-684",2008,"","","http://www2008.org/papers/pdf/p675-liA.pdf","Proceedings of the 17th International World Wide Web Conference","","","","","ACM","","","","","","","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.","","*** association clustering community del.icio.us detection folksonomy rules ","",""
6,"","conf/kdd/LiuHM98","Liu, Bing; Hsu, Wynne & Ma, Yiming","Integrating Classification and Association Rule Mining.","",,,"","80-86",1998,"","","http://www.comp.nus.edu.sg/~dm2/publications/kdd98_1.ps","KDD","","","","","","","","","","","","","","association classification combination mining rule ","",""
6,"","conf/sigmod/NgLHP98","Ng, Raymond T.; Lakshmanan, Laks V. S.; Han, Jiawei & Pang, Alex","Exploratory Mining and Pruning Optimizations of Constrained Association Rules.","",,,"","13-24",1998,"","","http://dblp.uni-trier.de/db/conf/sigmod/sigmod98.html#NgLHP98","SIGMOD Conference","","","","","","","","","","","","","","2006 association constraints kdd lecture mining rules ","",""
7,"","park1995ehb","Park, J.S.; Chen, M.S. & Yu, P.S.","An effective hash-based algorithm for mining association rules","Proceedings of the 1995 ACM SIGMOD international conference on Management of data",,,"","175-186",1995,"","","","","","","","","ACM Press New York, NY, USA","","","","","","","","","2006 association hash kdd lecture mining rules table ","",""
6,"978-3-540-34415-5","schmitz2006mining","Schmitz, Christoph; Hotho, Andreas; Jäschke, Robert & Stumme, Gerd","Mining Association Rules in Folksonomies","",,,"July","261-270",2006,"Ljubljana","","http://www.kde.cs.uni-kassel.de/hotho/pub/2006/schmitz2006asso_ifcs.pdf","Data Science and Classification (Proc. IFCS 2006 Conference)","","","Studies in Classification, Data Analysis, and Knowledge Organization","Batagelj, V.; Bock, H.-H.; Ferligoj, A. & Žiberna, A.","Springer","","","","","","","","","2006 analysis association folksonomy kdubiq myown network rules semantic seminar2006 sosbuch summerschool ","",""
6,"","SrikantAgrawal95","Srikant, R. & Agrawal, R.","Mining Generalized Association Rules","",,,"Sep","407--419",1995,"","","","Proceedings of the 21st International Conference on Very Large Databases","","","","","","","","","","","","","","association generalized mining rules ","",""