RakeshAgrawal RamakrishnanSrikant Fast Algorithms for Mining Association Rules in Large Databases Morgan Kaufmann Publishers Inc. 487–499 1994 PhilippeFlajolet G.NigelMartin Probabilistic Counting Algorithms for Data Base Applications Journal of Computer and System Sciences 31 182-209 1985 XinLi LeiGuo YihongE.Zhao Tag-based Social Interest Discovery ACM 675-684 2008 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. BingLiu WynneHsu YimingMa Integrating Classification and Association Rule Mining. 80-86 1998 RaymondT.Ng LaksV. S.Lakshmanan JiaweiHan AlexPang Exploratory Mining and Pruning Optimizations of Constrained Association Rules. 13-24 1998 J.S.Park M.S.Chen P.S.Yu An effective hash-based algorithm for mining association rules Proceedings of the 1995 ACM SIGMOD international conference on Management of data ACM Press New York, NY, USA 175-186 1995 ChristophSchmitz AndreasHotho RobertJäschke GerdStumme Mining Association Rules in Folksonomies Springer 261-270 2006 R.Srikant R.Agrawal Mining Generalized Association Rules 407–419 1995