http://www.bibsonomy.org/burst/concept/user/schmitz/dataminingBibSonomy BuRST Feed for /concept/user/schmitz/datamining2008-07-21T00:36:19+02:00dblphttp://www.bibsonomy.org/bibtex/2f353e7632080dda56af02830f90ad43c/schmitzschmitz2007-04-04T16:31:03+02:00implementation datamining apriori <span style="color:#555555;">Christian <a href="http://www.bibsonomy.org/author/Borgelt">Borgelt</a> </span><em>FIMI, </em><em>volume126ofCEUR Workshop Proceedings, </em><em>CEUR-WS.org, </em>(<em>2004</em>)Wed Apr 04 16:31:03 CEST 2007FIMIconf/fimi/2004CEUR Workshop ProceedingsRecursion Pruning for the Apriori Algorithm.1262004implementation datamining apriori references of talks, papers, seminarshttp://www.bibsonomy.org/bibtex/2cdba7839597627e65fcb2aef80f775e1/schmitzschmitz2006-11-15T15:45:12+01:00folksonomy tagging mining datamining bookmark <span style="color:#555555;">S. <a href="http://www.bibsonomy.org/author/Chakrabarti">Chakrabarti</a> and Y. <a href="http://www.bibsonomy.org/author/Batterywala">Batterywala</a> </span>(<em>2000</em>)Wed Nov 15 15:45:12 CET 2006BostonProc. SIGKDD KDD 2000 Workshop on Text MiningMining themes from bookmarks2000folksonomy tagging mining datamining bookmark Many Web surfers maintain bookmark files containing URLs organized into folders which represent topics by function or content. We propose a new framework for discovering themes among such folders created by a community of surfers with overlapping interests. In principle, surfers need not create personal folders; they can just use a 'standard' tax- onomy such as Yahoo. In practice, a standard taxonomy is neither necessary nor sufficient; it is too detailed in most part, but likely to be inadequate in the community's areas of deep interest. Our framework, based on the minimum de- scription length principle, exploits two semantically different relations: "term occurs in document" and "document occurs in folder", and combines them without the need for heuris- tic weighting of attributes. Themes, in effect, finds common factors in people's interests when it can, while maintaining their individuality when it must. We present experimen- tal results to illustrate our framework and show how it is different from content-based clustering. We also propose a number of applications to finding themes, including collab- orative recommendation and resource discovery.DBLP Record 'conf/itng/NiwaDH06'http://www.bibsonomy.org/bibtex/2c6db7b31889366b4671f6db9491a3d96/schmitzschmitz2006-11-15T15:26:27+01:00mining datamining folksonomy <span style="color:#555555;">Satoshi <a href="http://www.bibsonomy.org/author/Niwa">Niwa</a> and Takuo <a href="http://www.bibsonomy.org/author/Doi">Doi</a> and Shinichi <a href="http://www.bibsonomy.org/author/Honiden">Honiden</a> </span><em>ITNG, </em><em>page388-393. </em>(<em>2006</em>)Wed Nov 15 15:26:27 CET 2006ITNGDBLP:conf/itng/2006388-393Web Page Recommender System based on Folksonomy Mining for ITNG '06 Submissions.2006mining datamining folksonomy http://www.bibsonomy.org/bibtex/29723b092d975dedb8f6d5f711bb00ffd/schmitzschmitz2006-08-02T16:40:04+02:00hypergraph clustering datamining <span style="color:#555555;">Eui-Hong <a href="http://www.bibsonomy.org/author/Han">Han</a> and George <a href="http://www.bibsonomy.org/author/Karypis">Karypis</a> and Vipin <a href="http://www.bibsonomy.org/author/Kumar">Kumar</a> and Bamshad <a href="http://www.bibsonomy.org/author/Mobasher">Mobasher</a> </span><em>Data Engineering Bulletin</em><em>21(1):15-22</em>(<em>1998</em>)Wed Aug 02 16:40:04 CEST 2006Data Engineering Bulletin115-22Hypergraph Based Clustering in High-Dimensional Data Sets: A Summary of Results211998hypergraph clustering datamining