http://www.bibsonomy.org/burst/user/hotho/dmBibSonomy BuRST Feed for /user/hotho/dm2008-10-08T13:17:22+02:00SpringerLink - Buchkapitelhttp://www.bibsonomy.org/bibtex/227c7357d3337d890fef53168dce9ed33/hothohotho2008-09-11T12:03:43+02:00search ml analysis intention dm query toread <span style="color:#555555;">Ricardo <a href="http://www.bibsonomy.org/author/Baeza-Yates">Baeza-Yates</a> und Liliana <a href="http://www.bibsonomy.org/author/Calderón-Benavides">Calder&#243;n-Benavides</a> und Cristina <a href="http://www.bibsonomy.org/author/González-Caro">Gonz&#225;lez-Caro</a> </span><em>String Processing and Information Retrieval</em>(<em>2006</em>)Thu Sep 11 12:03:43 CEST 2008String Processing and Information Retrieval98--109The Intention Behind Web Queries2006search ml analysis intention dm query toread The identification of the user’s intention or interest through queries that they submit to a search engine can be very useful to offer them more adequate results. In this work we present a framework for the identification of user’s interest in an automaticway, based on the analysis of query logs. This identification is made from two perspectives, the objectives or goals of auser and the categories in which these aims are situated. A manual classification of the queries was made in order to havea reference point and then we applied supervised and unsupervised learning techniques. The results obtained show that fora considerable amount of cases supervised learning is a good option, however through unsupervised learning we found relationshipsbetween users and behaviors that are not easy to detect just taking the query words. Also, through unsupervised learning weestablished that there are categories that we are not able to determine in contrast with other classes that were not consideredbut naturally appear after the clustering process. This allowed us to establish that the combination of supervised and unsupervisedlearning is a good alternative to find user’s goals. From supervised learning we can identify the user interest given certainestablished goals and categories; on the other hand, with unsupervised learning we can validate the goals and categories used,refine them and select the most appropriate to the user’s needs.http://www.bibsonomy.org/bibtex/26357f535000a383f228f1e8e56ca86ca/hothohotho2008-08-26T08:37:58+02:00social 2008 myown classification bookmarking ml mining dm folksonomy spam <span style="color:#555555;">Beate <a href="http://www.bibsonomy.org/author/Krause">Krause</a> und Christoph <a href="http://www.bibsonomy.org/author/Schmitz">Schmitz</a> und Andreas <a href="http://www.bibsonomy.org/author/Hotho">Hotho</a> und Gerd <a href="http://www.bibsonomy.org/author/Stumme">Stumme</a> </span><em>Proc. of the Fourth International Workshop on Adversarial Information Retrieval on the Web, </em>(<em>2008</em>)Tue Aug 26 08:37:58 CEST 2008Proc. of the Fourth International Workshop on Adversarial Information Retrieval on the WebThe Anti-Social Tagger - Detecting Spam in Social Bookmarking Systems2008social 2008 myown classification bookmarking ml mining dm folksonomy spam Amazon.de: Text Mining. Predictive Methods for Analyzing Unstructured Information: Sholom M. Weiss,Nitin Indurkhya,T. Zhang: English Bookshttp://www.bibsonomy.org/bibtex/26ac07561b543e6033fd4c9811d0dccad/hothohotho2008-04-12T15:25:20+02:00software tm nlp mining text dm <span style="color:#555555;">Sholom M. <a href="http://www.bibsonomy.org/author/Weiss">Weiss</a> und Nitin <a href="http://www.bibsonomy.org/author/Indurkhya">Indurkhya</a> und T. <a href="http://www.bibsonomy.org/author/Zhang">Zhang</a> </span><em>Springer, Berlin, </em><em>1 Edition, </em>(<em>2004</em>)Sat Apr 12 15:25:20 CEST 20081Text Mining. Predictive Methods for Analyzing Unstructured Information2004software tm nlp mining text dm From Web to Social Web: Discovering and Deploying User and Cont... - Data Mi...Journals, Books & Online Media | Springerhttp://www.bibsonomy.org/bibtex/28aa8d9bcbb5a5bb3fc480d1e53b27236/hothohotho2008-01-31T12:01:31+01:00tm data web mining myown social dm 2007 <span style="color:#555555;">B. <a href="http://www.bibsonomy.org/author/Berendt">Berendt</a> und A. <a href="http://www.bibsonomy.org/author/Hotho">Hotho</a> und D. <a href="http://www.bibsonomy.org/author/Mladenic">Mladenic</a> und G. <a href="http://www.bibsonomy.org/author/Semeraro">Semeraro</a> (eds.). </span><em>LNCS </em><em>Springer, </em>(<em>2007</em>)Thu Jan 31 12:01:31 CET 2008LNCSFrom Web to Social Web: Discovering and Deploying User and Content Profiles 47362007tm data web mining myown social dm 2007 This book constitutes the refereed proceedings of the Workshop on Web Mining, WebMine 2006, held in Berlin, Germany, September 18th, 2006. Topics included are data mining based on analysis of bloggers and tagging, web mining, XML mining and further techniques of knowledge discovery. The book is especially valuable for those interested in the aspects of the Social Web (Web 2.0) and its inherent dynamic and diversity of user-generated content.Distributed feature extraction in a p2p settinghttp://www.bibsonomy.org/bibtex/2e5eba80e58b4532a3fd3bcf50994734e/hothohotho2008-01-25T20:42:37+01:00mining 2.0 summerschool music dm web p2p kdubiq tagging <span style="color:#555555;">Michael <a href="http://www.bibsonomy.org/author/Wurst">Wurst</a> und Katharina <a href="http://www.bibsonomy.org/author/Morik">Morik</a> </span><em>Future Gener. Comput. Syst.</em><em>23(1):69--75</em>(<em>2007</em>)Fri Jan 25 20:42:37 CET 2008Amsterdam, The Netherlands, The NetherlandsFuture Gener. Comput. Syst.169--75Distributed feature extraction in a p2p setting: a case study232007mining 2.0 summerschool music dm web p2p kdubiq tagging Probabilistic Counting Algorithms for Data Base Applications - Flajolet, Martin (ResearchIndex)http://www.bibsonomy.org/bibtex/27ccb5baa7d4e0836127b21468c92a27d/hothohotho2007-11-09T12:38:48+01:00rule kdubiq toread association dm counting <span style="color:#555555;">Philippe <a href="http://www.bibsonomy.org/author/Flajolet">Flajolet</a> und G. <a href="http://www.bibsonomy.org/author/Nigel Martin">Nigel Martin</a> </span><em>Journal of Computer and System Sciences</em><em>31(2):182-209</em>(<em>1985</em>)Fri Nov 09 12:38:48 CET 2007Journal of Computer and System Sciences2182-209Probabilistic Counting Algorithms for Data Base Applications311985rule kdubiq toread association dm counting dblphttp://www.bibsonomy.org/bibtex/27efa21cb8537359f6995cde9c307d181/hothohotho2007-10-28T22:18:15+01:00clustering toread communities soical graph networks dm <span style="color:#555555;">Hemant <a href="http://www.bibsonomy.org/author/Balakrishnan">Balakrishnan</a> und Narsingh <a href="http://www.bibsonomy.org/author/Deo">Deo</a> </span><em>ACM Southeast Regional Conference, </em><em>Seite280-285. </em><em>ACM, </em>(<em>2006</em>)Sun Oct 28 22:18:15 CET 2007ACM Southeast Regional Conferenceconf/ACMse/2006280-285Discovering communities in complex networks.2006clustering toread communities soical graph networks dm Amazon.com: Probabilistic Robotics (Intelligent Robotics and Autonomous Agents): Books: Sebastian Thrun,Wolfram Burgard,Dieter Foxhttp://www.bibsonomy.org/bibtex/2914a56f048c863f0928bb6d1efe09ff7/hothohotho2007-09-28T16:05:05+02:00dm probabilistic ml machine learning <span style="color:#555555;">Sebastian <a href="http://www.bibsonomy.org/author/Thrun">Thrun</a> und Wolfram <a href="http://www.bibsonomy.org/author/Burgard">Burgard</a> und Dieter <a href="http://www.bibsonomy.org/author/Fox">Fox</a> </span>(<em>2001</em>)Fri Sep 28 16:05:05 CEST 2007Probabilistic Robotics (Intelligent Robotics and Autonomous Agents)2001dm probabilistic ml machine learning http://www.bibsonomy.org/bibtex/20e71ddd52894af0e681b9d9411f7944f/hothohotho2007-06-05T09:54:45+02:00ml learning web ol dm semantic <span style="color:#555555;">Paul <a href="http://www.bibsonomy.org/author/Buitelaar">Buitelaar</a> und Philipp <a href="http://www.bibsonomy.org/author/Cimiano">Cimiano</a> und Bernardo <a href="http://www.bibsonomy.org/author/Magnini">Magnini</a> (eds.). </span><em>Frontiers in Artificial Intelligence </em><em>IOS Press, </em><em>JUL2005. </em>Tue Jun 05 09:54:45 CEST 2007JULFrontiers in Artificial IntelligenceOntology Learning from Text: Methods, Evaluation and Applications1232005ml learning web ol dm semantic http://www.bibsonomy.org/bibtex/23e4e2899e7d6988218d02a264bcfe24a/hothohotho2007-05-18T19:27:34+02:00web dm kdd mining KI2007WebMining ml <span style="color:#555555;">Pierre <a href="http://www.bibsonomy.org/author/Baldi">Baldi</a> und Paolo <a href="http://www.bibsonomy.org/author/Frasconi">Frasconi</a> und Padhraic <a href="http://www.bibsonomy.org/author/Smyth">Smyth</a> </span><em>Wiley, </em><em>April2003. </em>Fri May 18 19:27:34 CEST 2007Modeling the Internet and the Web: Probabilistic Methods and AlgorithmsAprilModeling the Internet and the Web: Probabilistic Methods and Algorithms2003web dm kdd mining KI2007WebMining ml Modeling the Internet and the Web covers the most important aspects of modeling the Web using a modern mathematical and probabilistic treatment. It focuses on the information and application layers, as well as some of the emerging properties of the Internet.  Provides a comprehensive introduction to the modeling of the Internet and the Web at the information level.  Takes a modern approach based on mathematical, probabilistic, and graphical modeling.  Provides an integrated presentation of theory, examples, exercises and applications.  Covers key topics such as text analysis, link analysis, crawling techniques, human behaviour, and commerce on the Web. Interdisciplinary in nature, Modeling the Internet and the Web will be of interest to students and researchers from a variety of disciplines including computer science, machine learning, engineering, statistics, economics, business, and the social sciences.DBLP Record 'conf/kdd/1999web'http://www.bibsonomy.org/bibtex/218a9697e8ca04f637487e79b6be9cc83/hothohotho2007-05-18T19:17:00+02:00webkdd KI2007WebMining workshop kdd mining web dm <span style="color:#555555;">Brij M. <a href="http://www.bibsonomy.org/author/Masand">Masand</a> und Myra <a href="http://www.bibsonomy.org/author/Spiliopoulou">Spiliopoulou</a> (eds.). </span><em>Volume1836vonLecture Notes in Computer Science, </em><em>Springer, </em>(<em>2000</em>)Fri May 18 19:17:00 CEST 2007WEBKDDLecture Notes in Computer ScienceWeb Usage Analysis and User Profiling, International WEBKDD'99 Workshop, San Diego, California, USA, August 15, 1999, Revised Papers18362000webkdd KI2007WebMining workshop kdd mining web dm dblphttp://www.bibsonomy.org/bibtex/229f6bc4833269393dabf92bae3afa905/hothohotho2007-04-26T14:52:54+02:00wg4 kdd preparation kdubiq dm mining data <span style="color:#555555;">Dorian <a href="http://www.bibsonomy.org/author/Pyle">Pyle</a> </span><em>Morgan Kaufmann, </em>(<em>1999</em>)Thu Apr 26 14:52:54 CEST 2007Data Preparation for Data Mining1999wg4 kdd preparation kdubiq dm mining data State of the art of graph-based data mininghttp://www.bibsonomy.org/bibtex/20c5107f6feec6fcdc2e791a0b12ce448/hothohotho2007-02-12T21:48:01+01:00toread dm graph <span style="color:#555555;">Takashi <a href="http://www.bibsonomy.org/author/Washio">Washio</a> und Hiroshi <a href="http://www.bibsonomy.org/author/Motoda">Motoda</a> </span><em>SIGKDD Explorations</em><em>5(1):59--68</em>(<em>2003</em>)Mon Feb 12 21:48:01 CET 2007New York, NY, USASIGKDD Explorations159--68State of the art of graph-based data mining52003toread dm graph The need for mining structured data has increased in the past few years. One of the best studied data structures in computer science and discrete mathematics are graphs. It can therefore be no surprise that graph based data mining has become quite popular in the last few years.This article introduces the theoretical basis of graph based data mining and surveys the state of the art of graph-based data mining. Brief descriptions of some representative approaches are provided as well. Wikipedia XML Corpushttp://www.bibsonomy.org/bibtex/2493b849942fcaf9ba8e8e68e3cb46d38/hothohotho2007-01-17T15:56:27+01:00corpus xml dm ml mining wikipedia data <span style="color:#555555;">Ludovic <a href="http://www.bibsonomy.org/author/Denoyer">Denoyer</a> und Patrick <a href="http://www.bibsonomy.org/author/Gallinari">Gallinari</a> </span><em>SIGIR Forum</em>(<em>2006</em>)Wed Jan 17 15:56:27 CET 2007SIGIR Forum{T}he {W}ikipedia {X}{M}{L} {C}orpus2006corpus xml dm ml mining wikipedia data UCI Machine Learning Repositoryhttp://www.bibsonomy.org/bibtex/285308db3df761f63f16a7cab4eb8d4aa/hothohotho2006-06-23T07:06:19+02:00dm data machine uci dataset ml learning mining <span style="color:#555555;">C.L. <a href="http://www.bibsonomy.org/author/Blake D.J. Newman">Blake D.J. Newman</a> und C.J. <a href="http://www.bibsonomy.org/author/Merz">Merz</a> </span>(<em>1998</em>)Fri Jun 23 07:06:19 CEST 2006{UCI} Repository of machine learning databases1998dm data machine uci dataset ml learning mining dblphttp://www.bibsonomy.org/bibtex/23553c3acc971c03813352c40afe7476a/hothohotho2006-04-17T20:02:28+02:00lecture mining discovery kdd definition dm knowledge 2006 data <span style="color:#555555;">Usama M. <a href="http://www.bibsonomy.org/author/Fayyad">Fayyad</a> und Gregory <a href="http://www.bibsonomy.org/author/Piatetsky-Shapiro">Piatetsky-Shapiro</a> und Padhraic <a href="http://www.bibsonomy.org/author/Smyth">Smyth</a> und Ramasamy <a href="http://www.bibsonomy.org/author/Uthurusamy">Uthurusamy</a> (eds.). </span><em>AAAI/MIT Press, </em>(<em>1996</em>)Mon Apr 17 20:02:28 CEST 2006Advances in Knowledge Discovery and Data Mining.1996lecture mining discovery kdd definition dm knowledge 2006 data The big onehttp://www.bibsonomy.org/bibtex/2eb5f2e6742d9520453cbf9d100cacfd2/hothohotho2006-04-17T19:43:11+02:002006 kdd definition dm lecture <span style="color:#555555;">U. <a href="http://www.bibsonomy.org/author/Fayyad">Fayyad</a> und G. <a href="http://www.bibsonomy.org/author/Piatetsky-Shapiro">Piatetsky-Shapiro</a> und P. <a href="http://www.bibsonomy.org/author/Smyth">Smyth</a> </span><em>AI Magazine</em>(<em>1996</em>)Mon Apr 17 19:43:11 CEST 2006AI Magazine37-54From Data Mining to Knowledge Discovery in Databases19962006 kdd definition dm lecture http://www.bibsonomy.org/bibtex/23e79734ee1a6e49aee02ffd108224d1c/hothohotho2006-04-17T19:22:02+02:00ml dm kdd 2006 lecture introduction survey <span style="color:#555555;">Tom M. <a href="http://www.bibsonomy.org/author/Mitchell">Mitchell</a> </span><em>McGraw-Hill, </em>(<em>1997</em>)Mon Apr 17 19:22:02 CEST 2006Machine Learning1997ml dm kdd 2006 lecture introduction survey http://www.bibsonomy.org/bibtex/25c9b8ae716d306921bb1c64cefa2ab84/hothohotho2006-04-17T19:10:30+02:002006 lecture statistic dm kdd <span style="color:#555555;">R. <a href="http://www.bibsonomy.org/author/Maitra">Maitra</a> </span><em>J. Ind. Soc. Prob. Statist.</em>(<em>2002</em>)Mon Apr 17 19:10:30 CEST 2006J. Ind. Soc. Prob. Statist.maitra02A Statistical Perspective on Data MiningArticle20022006 lecture statistic dm kdd http://www.bibsonomy.org/bibtex/2e2e195a4102d09f2ed4976bf102af01d/hothohotho2005-12-20T20:21:42+01:00introduction dm data mining <span style="color:#555555;">D. <a href="http://www.bibsonomy.org/author/Hand">Hand</a> und H. <a href="http://www.bibsonomy.org/author/Mannila">Mannila</a> und P. <a href="http://www.bibsonomy.org/author/Smyth">Smyth</a> </span><em>Cambridge, MA: MIT Press, </em>(<em>2001</em>)Tue Dec 20 20:21:42 CET 2005Principles of Data Mining2001introduction dm data mining