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,"978-1-59593-654-7","ahn2007topological","Ahn, Yong-Yeol; Han, Seungyeop; Kwak, Haewoon; Moon, Sue & Jeong, Hawoong","Analysis of topological characteristics of huge online social networking services","",,,"","835--844",2007,"New York, NY, USA","","http://portal.acm.org/citation.cfm?id=1242685","Proceedings of the 16th international conference on World Wide Web","","","","","ACM","","","","","","","","","analysis folksonomy network online social ","",""
7,"","611918","Astrachan, Owen","Bubble sort: an archaeological algorithmic analysis","SIGCSE Bull.",35,1,"","1--5",2003,"New York, NY, USA","","http://portal.acm.org/citation.cfm?id=611918&dl=GUIDE,","","","","","","ACM","","","","","","","Text books, including books for general audiences, invariably mention bubble sort in discussions of elementary sorting algorithms. We trace the history of bubble sort, its popularity, and its endurance in the face of pedagogical assertions that code and algorithmic examples used in early courses should be of high quality and adhere to established best practices. This paper is more an historical analysis than a philosophical treatise for the exclusion of bubble sort from books and courses. However, sentiments for exclusion are supported by Knuth [17], "In short, the bubble sort seems to have nothing to recommend it, except a catchy name and the fact that it leads to some interesting theoretical problems." Although bubble sort may not be a best practice sort, perhaps the weight of history is more than enough to compensate and provide for its longevity.","","algorithm bubblesort history sorting ","",""
1,"3-89139-646-3","Goossens2000dlb","Goossens, Michel; Mittelbach, Frank & Samarin, Alexander","Der \LaTeX-Begleiter","",,,"","",1994,"M{\"u}nchen","","","","","","","","Addison-Wesley","","","","","","","","","latex manual seminar tex ","",""
6,"","Detecting_Commmunities_via_Simultaneous_Clustering_of_Graphs_and_Folksonomies","Java, Akshay; Joshi, Anupam & Finin, Tim","Detecting Commmunities via Simultaneous Clustering of Graphs and Folksonomies","",,,"August","",2008,"","To Appear","","WebKDD 2008 Workshop on Web Mining and Web Usage Analysis","","","","","","","","","","","","","","clustering community detection ","",""
6,"0-7695-2701-9","jaeschke2006trias","Jäschke, Robert; Hotho, Andreas; Schmitz, Christoph; Ganter, Bernhard & Stumme, Gerd","TRIAS - An Algorithm for Mining Iceberg Tri-Lattices","",,,"","907--911",2006,"Washington, DC, USA","","http://portal.acm.org/citation.cfm?id=1193256","ICDM '06: Proceedings of the Sixth International Conference on Data Mining","","","","","IEEE Computer Society","","","","","","","In this paper, we present the foundations for mining frequent tri-concepts, which extend the notion of closed itemsets to three-dimensional data to allow for mining folk-sonomies. We provide a formal definition of the problem, and present an efficient algorithm for its solution as well as experimental results on a large real-world example.","","2006 myown trias ","",""
6,"","Jaeschke2008logsonomy","Jäschke, Robert; Krause, Beate; Hotho, Andreas & Stumme, Gerd","Logsonomy — A Search Engine Folksonomy","",,,"","",2008,"","","http://www.kde.cs.uni-kassel.de/hotho/pub/2008/Krause2008logsonomy_short.pdf","Proceedings of the Second International Conference on Weblogs and Social Media(ICWSM 2008)","","","","","AAAI Press","","","","","","","In social bookmarking systems users describe bookmarks by keywords called tags. The structure behind these social systems, called folksonomies, can be viewed as a tripartite hypergraph of user, tag and resource nodes. This underlying network shows specific structural properties that explain its growth and the possibility of serendipitous exploration. Search engines filter the vast information of the web. Queries describe a user’s information need. In response to the displayed results of the search engine, users click on the links of the result page as they expect the answer to be of relevance. The clickdata can be represented as a folksonomy in which queries are descriptions of clicked URLs. This poster analyzes the topological characteristics of the resulting tripartite hypergraph of queries, users and bookmarks of two query logs and compares it two a snapshot of the folksonomy del.icio.us.","","2008 engine folksonomy l3s logsonomy myown search wp5 ","",""
1,"3-89319-434-7","kopka:1988","Kopka, Helmut","\LaTeX: Eine Einführung","",,,"","",1992,"Bonn, Paris","","","","","","","","Addison-Wesley","","","","","","","","","latex manual seminar tex ","",""
6,"978-1-59593-985-2","krause2008logsonomy","Krause, Beate; Jäschke, Robert; Hotho, Andreas & Stumme, Gerd","Logsonomy - Social Information Retrieval with Logdata","",,,"","157--166",2008,"New York, NY, USA","","http://portal.acm.org/citation.cfm?id=1379092.1379123&coll=ACM&dl=ACM&type=series&idx=SERIES399&part=series&WantType=Journals&title=Proceedings%20of%20the%20nineteenth%20ACM%20conference%20on%20Hypertext%20and%20hypermedia","HT '08: Proceedings of the nineteenth ACM conference on Hypertext and hypermedia","","","","","ACM","","","","","","","Social bookmarking systems constitute an established part of the Web 2.0. In such systems users describe bookmarks by keywords called tags. The structure behind these social systems, called folksonomies, can be viewed as a tripartite hypergraph of user, tag and resource nodes. This underlying network shows specific structural properties that explain its growth and the possibility of serendipitous exploration. Today’s search engines represent the gateway to retrieve information from the World Wide Web. Short queries typically consisting of two to three words describe a user’s information need. In response to the displayed results of the search engine, users click on the links of the result page as they expect the answer to be of relevance. This clickdata can be represented as a folksonomy in which queries are descriptions of clicked URLs. The resulting network structure, which we will term logsonomy is very similar to the one of folksonomies. In order to find out about its properties, we analyze the topological characteristics of the tripartite hypergraph of queries, users and bookmarks on a large snapshot of del.icio.us and on query logs of two large search engines. All of the three datasets show small world properties. The tagging behavior of users, which is explained by preferential attachment of the tags in social bookmark systems, is reflected in the distribution of single query words in search engines. We can conclude that the clicking behaviour of search engine users based on the displayed search results and the tagging behaviour of social bookmarking users is driven by similar dynamics.","","analysis engine information l3s logsonomy network retrieval search social wp5 ","",""
7,"","tkde06","Lucchese, Claudio; Orlando, Salvatore & Perego, Raffaele","Fast and Memory Efficient Mining of Frequent Closed Itemsets","IEEE Transactions On Knowledge and Data Engineering",18,1,"","21--36",2006,"","","","","","","","","","","","","","","","","","association closed fca frequent itemset mining rule ","",""
6,"","orlando02efficient","Orlando, Salvatore; Palmerini, Paolo; Perego, Raffaele & Silvestri, Fabrizio","An Efficient Parallel and Distributed Algorithm for Counting Frequent Sets","",,,"","3--29",2003,"","","http://dx.doi.org/10.1007/3-540-36569-9_28","High Performance Computing for Computational Science — VECPAR 2002","","","","","","","","","","","","Due to the huge increase in the number and dimension of available databases, efficient solutions for counting frequent sets are nowadays very important within the Data Mining community. Several sequential and parallel algorithms were proposed, whichin many cases exhibit excellent scalability. In this paper we present ParDCI, a distributed and multithreaded algorithm forcounting the occurrences of frequent sets within transactional databases. ParDCI is a parallel version of DCI (Direct Count& Intersect), a multi-strategy algorithm which is able to adapt its behavior not only to the features of the specific computingplatform (e.g. available memory), but also to the features of the dataset being processed (e.g. sparse or dense datasets).ParDCI enhances previous proposals by exploiting the highly optimized counting and intersection techniques of DCI, and byrelying on a multi-level parallelization approachwh ichex plicitly targets clusters of SMPs, an emerging computing platform.We focused our work on the efficient exploitation of the underlying architecture. Intra-Node multithreading effectively exploitsthe memory hierarchies of each SMP node, while Inter-Node parallelism exploits smart partitioning techniques aimed at reducingcommunication overheads. In depth experimental evaluations demonstrate that ParDCI reaches nearly optimal performances undera variety of conditions.","","algorithm fca frequent itemset mining parallel set ","",""