PUMA publications for /tag/datasethttps://puma.uni-kassel.de/tag/datasetPUMA RSS feed for /tag/dataset2024-03-28T18:59:39+01:00An Overview of Microsoft Academic Service (MAS) and Applications.https://puma.uni-kassel.de/bibtex/2e6066395c31b2f3de9fb836dbac5723a/hothohotho2015-09-15T16:06:26+02:00MSAC dataset toread <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Arnab Sinha" itemprop="url" href="/author/Arnab%20Sinha"><span itemprop="name">A. Sinha</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Zhihong Shen" itemprop="url" href="/author/Zhihong%20Shen"><span itemprop="name">Z. Shen</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Yang Song" itemprop="url" href="/author/Yang%20Song"><span itemprop="name">Y. Song</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Hao Ma" itemprop="url" href="/author/Hao%20Ma"><span itemprop="name">H. Ma</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Darrin Eide" itemprop="url" href="/author/Darrin%20Eide"><span itemprop="name">D. Eide</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Bo-June Paul Hsu" itemprop="url" href="/author/Bo-June%20Paul%20Hsu"><span itemprop="name">B. Hsu</span></a></span>, und <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Kuansan Wang" itemprop="url" href="/author/Kuansan%20Wang"><span itemprop="name">K. Wang</span></a></span>. </span><span itemtype="http://schema.org/Book" itemscope="itemscope" itemprop="isPartOf"><em><span itemprop="name">WWW (Companion Volume)</span>, </em></span><em>Seite <span itemprop="pagination">243-246</span>. </em><em><span itemprop="publisher">ACM</span>, </em>(<em><span>2015<meta content="2015" itemprop="datePublished"/></span></em>)Tue Sep 15 16:06:26 CEST 2015WWW (Companion Volume)conf/www/2015c243-246An Overview of Microsoft Academic Service (MAS) and Applications.2015MSAC dataset toread Impact of Data Characteristics on Recommender Systems Performancehttps://puma.uni-kassel.de/bibtex/2e41453a56391ca382f2298607b361208/stephandoerfelstephandoerfel2014-08-06T18:12:26+02:00characteristics dataset dependence evaluation model recommender <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Gediminas Adomavicius" itemprop="url" href="/author/Gediminas%20Adomavicius"><span itemprop="name">G. Adomavicius</span></a></span>, und <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Jingjing Zhang" itemprop="url" href="/author/Jingjing%20Zhang"><span itemprop="name">J. Zhang</span></a></span>. </span><span itemtype="http://schema.org/PublicationIssue" itemscope="itemscope" itemprop="isPartOf"><span itemtype="http://schema.org/Periodical" itemscope="itemscope" itemprop="isPartOf"><span itemprop="name"><em>ACM Trans. Manage. Inf. Syst.</em></span></span> <em><span itemtype="http://schema.org/PublicationVolume" itemscope="itemscope" itemprop="isPartOf"><span itemprop="volumeNumber">3 </span></span>(<span itemprop="issueNumber">1</span>):
<span itemprop="pagination">3:1--3:17</span></em> </span>(<em><span>April 2012<meta content="April 2012" itemprop="datePublished"/></span></em>)Wed Aug 06 18:12:26 CEST 2014New York, NY, USAACM Trans. Manage. Inf. Syst.apr13:1--3:17Impact of Data Characteristics on Recommender Systems Performance32012characteristics dataset dependence evaluation model recommender This article investigates the impact of rating data characteristics on the performance of several popular recommendation algorithms, including user-based and item-based collaborative filtering, as well as matrix factorization. We focus on three groups of data characteristics: rating space, rating frequency distribution, and rating value distribution. A sampling procedure was employed to obtain different rating data subsamples with varying characteristics; recommendation algorithms were used to estimate the predictive accuracy for each sample; and linear regression-based models were used to uncover the relationships between data characteristics and recommendation accuracy. Experimental results on multiple rating datasets show the consistent and significant effects of several data characteristics on recommendation accuracy.Impact of data characteristics on recommender systems performanceHarnessing Folksonomies to Produce a Social Classification of Resourceshttps://puma.uni-kassel.de/bibtex/28a25332bfeb33e2ad8e1e1a062976da2/hothohotho2013-05-04T16:10:09+02:00classification delicious folksonomy tagging toread dataset <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Arkaitz Zubiaga" itemprop="url" href="/author/Arkaitz%20Zubiaga"><span itemprop="name">A. Zubiaga</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Victor Fresno" itemprop="url" href="/author/Victor%20Fresno"><span itemprop="name">V. Fresno</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Raquel Martinez" itemprop="url" href="/author/Raquel%20Martinez"><span itemprop="name">R. Martinez</span></a></span>, und <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Alberto P. Garcia-Plaza" itemprop="url" href="/author/Alberto%20P.%20Garcia-Plaza"><span itemprop="name">A. Garcia-Plaza</span></a></span>. </span><span itemtype="http://schema.org/PublicationIssue" itemscope="itemscope" itemprop="isPartOf"><span itemtype="http://schema.org/Periodical" itemscope="itemscope" itemprop="isPartOf"><span itemprop="name"><em>IEEE Transactions on Knowledge and Data Engineering</em></span></span> </span>(<em><span>2012<meta content="2012" itemprop="datePublished"/></span></em>)Sat May 04 16:10:09 CEST 2013Los Alamitos, CA, USAIEEE Transactions on Knowledge and Data EngineeringPrePrintsHarnessing Folksonomies to Produce a Social Classification of Resources992012classification delicious folksonomy tagging toread dataset The Scholarly Database and its utility for scientometrics researchhttps://puma.uni-kassel.de/bibtex/2c24611ec1f2efbdcf7f5b26d49af320e/jaeschkejaeschke2013-04-24T11:17:52+02:00analysis database dataset gaw science scientometrics sdb sota <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Gavin La Rowe" itemprop="url" href="/author/Gavin%20La%20Rowe"><span itemprop="name">G. La Rowe</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Sumeet Ambre" itemprop="url" href="/author/Sumeet%20Ambre"><span itemprop="name">S. Ambre</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="John Burgoon" itemprop="url" href="/author/John%20Burgoon"><span itemprop="name">J. Burgoon</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Weimao Ke" itemprop="url" href="/author/Weimao%20Ke"><span itemprop="name">W. Ke</span></a></span>, und <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Katy Börner" itemprop="url" href="/author/Katy%20B%c3%b6rner"><span itemprop="name">K. Börner</span></a></span>. </span><span itemtype="http://schema.org/PublicationIssue" itemscope="itemscope" itemprop="isPartOf"><span itemtype="http://schema.org/Periodical" itemscope="itemscope" itemprop="isPartOf"><span itemprop="name"><em>Scientometrics</em></span></span> <em><span itemtype="http://schema.org/PublicationVolume" itemscope="itemscope" itemprop="isPartOf"><span itemprop="volumeNumber">79 </span></span>(<span itemprop="issueNumber">2</span>):
<span itemprop="pagination">219--234</span></em> </span>(<em><span>2009<meta content="2009" itemprop="datePublished"/></span></em>)Wed Apr 24 11:17:52 CEST 2013Scientometrics2219--234The Scholarly Database and its utility for scientometrics research792009analysis database dataset gaw science scientometrics sdb sota The Scholarly Database aims to serve researchers and practitioners interested in the analysis, modelling, and visualization of large-scale data sets. A specific focus of this database is to support macro-evolutionary studies of science and to communicate findings via knowledge-domain visualizations. Currently, the database provides access to about 18 million publications, patents, and grants. About 90% of the publications are available in full text. Except for some datasets with restricted access conditions, the data can be retrieved in raw or pre-processed formats using either a web-based or a relational database client. This paper motivates the need for the database from the perspective of bibliometric/scientometric research. It explains the database design, setup, etc., and reports the temporal, geographical, and topic coverage of data sets currently served via the database. Planned work and the potential for this database to become a global testbed for information science research are discussed at the end of the paper.Web Text Corpus for Natural Language Processing.https://puma.uni-kassel.de/bibtex/2934a38bd8d7696cff1da3a2df3724407/benzbenz2011-02-04T16:10:18+01:00corpus dataset web synonym_detection nlp <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Vinci Liu" itemprop="url" href="/author/Vinci%20Liu"><span itemprop="name">V. Liu</span></a></span>, und <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="James R. Curran" itemprop="url" href="/author/James%20R.%20Curran"><span itemprop="name">J. Curran</span></a></span>. </span><span itemtype="http://schema.org/Book" itemscope="itemscope" itemprop="isPartOf"><em><span itemprop="name">EACL</span>, </em></span><em><span itemprop="publisher">The Association for Computer Linguistics</span>, </em>(<em><span>2006<meta content="2006" itemprop="datePublished"/></span></em>)Fri Feb 04 16:10:18 CET 2011EACLconf/eacl/2006Web Text Corpus for Natural Language Processing.2006corpus dataset web synonym_detection nlp dblpA Large-Scale Study of MySpace:
Observations and Implications for Online Social Networkshttps://puma.uni-kassel.de/bibtex/256c414dfc572c2b0c5cbf48458c744b5/hothohotho2009-04-25T10:09:02+02:00analysis dataset myspace networking social <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="James Caverlee" itemprop="url" href="/author/James%20Caverlee"><span itemprop="name">J. Caverlee</span></a></span>, und <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Steve Webb" itemprop="url" href="/author/Steve%20Webb"><span itemprop="name">S. Webb</span></a></span>. </span><span itemtype="http://schema.org/Book" itemscope="itemscope" itemprop="isPartOf"><em><span itemprop="name">Proceedings from the 2nd International Conference on Weblogs and Social Media (AAAI)</span>, </em></span>(<em><span>2008<meta content="2008" itemprop="datePublished"/></span></em>)Sat Apr 25 10:09:02 CEST 2009Proceedings from the 2nd International Conference on Weblogs and Social Media (AAAI)A Large-Scale Study of MySpace:
Observations and Implications for Online Social Networks2008analysis dataset myspace networking social CiteULike: A Large-Scale Study of MySpace: Observations and Implications for Online Social NetworksRobust De-anonymization of Large Sparse Datasetshttps://puma.uni-kassel.de/bibtex/22748ba4684dbe09120aee56c6a0a9de9/jaeschkejaeschke2009-04-14T16:38:11+02:00anonymization datamining dataset netflix privacy recommender toread <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Arvind Narayanan" itemprop="url" href="/author/Arvind%20Narayanan"><span itemprop="name">A. Narayanan</span></a></span>, und <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Vitaly Shmatikov" itemprop="url" href="/author/Vitaly%20Shmatikov"><span itemprop="name">V. Shmatikov</span></a></span>. </span><span itemtype="http://schema.org/Book" itemscope="itemscope" itemprop="isPartOf"><em><span itemprop="name">Proc. of the 29th IEEE Symposium on Security and Privacy</span>, </em></span><em>Seite <span itemprop="pagination">111--125</span>. </em><em><span itemprop="publisher">IEEE Computer Society</span>, </em>(<em><span>Mai 2008<meta content="Mai 2008" itemprop="datePublished"/></span></em>)Tue Apr 14 16:38:11 CEST 2009Proc. of the 29th IEEE Symposium on Security and Privacymay111--125Robust De-anonymization of Large Sparse Datasets2008anonymization datamining dataset netflix privacy recommender toread We present a new class of statistical de- anonymization attacks against high-dimensional micro-data, such as individual preferences, recommendations, transaction records and so on. Our techniques are robust to perturbation in the data and tolerate some mistakes in the adversary's background knowledge. We apply our de-anonymization methodology to the Netflix Prize dataset, which contains anonymous movie ratings of 500,000 subscribers of Netflix, the world's largest online movie rental service. We demonstrate that an adversary who knows only a little bit about an individual subscriber can easily identify this subscriber's record in the dataset. Using the Internet Movie Database as the source of background knowledge, we successfully identified the Netflix records of known users, uncovering their apparent political preferences and other potentially sensitive information.Improving Tag-Clouds as Visual Information Retrieval Interfaceshttps://puma.uni-kassel.de/bibtex/206f68f9fe46dc6d0f646d932e428dec9/hothohotho2009-01-16T12:08:23+01:00clouds dataset del.icio.us information tag tagging taggingsurvey toread visual <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Y. Hassan-Montero" itemprop="url" href="/author/Y.%20Hassan-Montero"><span itemprop="name">Y. Hassan-Montero</span></a></span>, und <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="V. Herrero-Solana" itemprop="url" href="/author/V.%20Herrero-Solana"><span itemprop="name">V. Herrero-Solana</span></a></span>. </span><span itemtype="http://schema.org/Book" itemscope="itemscope" itemprop="isPartOf"><em><span itemprop="name">InScit2006: International Conference on Multidisciplinary Information Sciences and Technologies</span>, </em></span>(<em><span>2006<meta content="2006" itemprop="datePublished"/></span></em>)Fri Jan 16 12:08:23 CET 2009InScit2006: International Conference on Multidisciplinary Information Sciences and TechnologiesImproving Tag-Clouds as Visual Information Retrieval Interfaces2006clouds dataset del.icio.us information tag tagging taggingsurvey toread visual Tagging-based systems enable users to categorize web resources by means of tags (freely chosen keywords), in order to re-finding these resources later. Tagging is implicitly also a social indexing process, since users share their tags and resources, constructing a social tag index, so-called folksonomy. At the same time of tagging-based system, has been popularised an interface model for visual information retrieval known as Tag-Cloud. In this model, the most frequently used tags are displayed in alphabetical order. This paper presents a novel approach to Tag-Cloud�s tags selection, and proposes the use of clustering algorithms for visual layout, with the aim of improve browsing experience. The results suggest that presented approach reduces the semantic density of tag set, and improves the visual consistency of Tag-Cloud layout.Folksonomies and clustering in the collaborative system CiteULikehttps://puma.uni-kassel.de/bibtex/22a219a2664c566b405420f720583643a/hothohotho2009-01-16T11:50:31+01:00*** citeulike clustering dataset folksonomy network properties <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Andrea Capocci" itemprop="url" href="/author/Andrea%20Capocci"><span itemprop="name">A. Capocci</span></a></span>, und <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Guido Caldarelli" itemprop="url" href="/author/Guido%20Caldarelli"><span itemprop="name">G. Caldarelli</span></a></span>. </span><span itemtype="http://schema.org/PublicationIssue" itemscope="itemscope" itemprop="isPartOf"><span itemtype="http://schema.org/Periodical" itemscope="itemscope" itemprop="isPartOf"><span itemprop="name"><em>Journal of Physics A: Mathematical and Theoretical</em></span></span> <em><span itemtype="http://schema.org/PublicationVolume" itemscope="itemscope" itemprop="isPartOf"><span itemprop="volumeNumber">41 </span></span>(<span itemprop="issueNumber">22</span>):
<span itemprop="pagination">224016 (7pp)</span></em> </span>(<em><span>2008<meta content="2008" itemprop="datePublished"/></span></em>)Fri Jan 16 11:50:31 CET 2009Journal of Physics A: Mathematical and Theoretical22224016 (7pp)Folksonomies and clustering in the collaborative system CiteULike412008*** citeulike clustering dataset folksonomy network properties We analyze CiteULike, an online collaborative tagging system where users bookmark and annotate scientific papers. Such a system can be naturally represented as a tri-partite graph whose nodes represent papers, users and tags connected by individual tag assignments. The semantics of tags is studied here, in order to uncover the hidden relationships between tags. We find that the clustering coefficient can be used to analyze the semantical patterns among tags.A sparse gaussian processes classification framework for fast tag suggestionshttps://puma.uni-kassel.de/bibtex/2d330a3537b4a14fbd40661424ec8e465/hothohotho2008-12-01T15:38:40+01:00bibsonomy bookmarking classification dataset ml recommender social tag tagging taggingsurvey toread <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Yang Song" itemprop="url" href="/author/Yang%20Song"><span itemprop="name">Y. Song</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Lu Zhang" itemprop="url" href="/author/Lu%20Zhang"><span itemprop="name">L. Zhang</span></a></span>, und <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="C. Lee Giles" itemprop="url" href="/author/C.%20Lee%20Giles"><span itemprop="name">C. Giles</span></a></span>. </span><span itemtype="http://schema.org/Book" itemscope="itemscope" itemprop="isPartOf"><em><span itemprop="name">CIKM '08: Proceeding of the 17th ACM conference on Information and knowledge mining</span>, </em></span><em>Seite <span itemprop="pagination">93--102</span>. </em><em>New York, NY, USA, </em><em><span itemprop="publisher">ACM</span>, </em>(<em><span>2008<meta content="2008" itemprop="datePublished"/></span></em>)Mon Dec 01 15:38:40 CET 2008New York, NY, USACIKM '08: Proceeding of the 17th ACM conference on Information and knowledge mining93--102A sparse gaussian processes classification framework for fast tag suggestions2008bibsonomy bookmarking classification dataset ml recommender social tag tagging taggingsurvey toread A sparse gaussian processes classification framework for fast tag suggestionsSemantic feature production norms for a large set of living and nonliving thingshttps://puma.uni-kassel.de/bibtex/2936af12b025e37b0a6aac6bc103f58a3/hothohotho2008-05-08T12:17:01+02:00dataset grounding ol ontology relation semantic toread <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="K McRae" itemprop="url" href="/author/K%20McRae"><span itemprop="name">K. McRae</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="G S Cree" itemprop="url" href="/author/G%20S%20Cree"><span itemprop="name">G. Cree</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="M S Seidenberg" itemprop="url" href="/author/M%20S%20Seidenberg"><span itemprop="name">M. Seidenberg</span></a></span>, und <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="C McNorgan" itemprop="url" href="/author/C%20McNorgan"><span itemprop="name">C. McNorgan</span></a></span>. </span><span itemtype="http://schema.org/PublicationIssue" itemscope="itemscope" itemprop="isPartOf"><span itemtype="http://schema.org/Periodical" itemscope="itemscope" itemprop="isPartOf"><span itemprop="name"><em>Behav Res Methods</em></span></span> <em><span itemtype="http://schema.org/PublicationVolume" itemscope="itemscope" itemprop="isPartOf"><span itemprop="volumeNumber">37 </span></span>(<span itemprop="issueNumber">4</span>):
<span itemprop="pagination">547-559</span></em> </span>(<em><span>November 2005<meta content="November 2005" itemprop="datePublished"/></span></em>)Thu May 08 12:17:01 CEST 2008Behav Res MethodsNov4547-559Semantic feature production norms for a large set of living and nonliving things372005dataset grounding ol ontology relation semantic toread Semantic features have provided insight into numerous behavioral phenomena concerning concepts, categorization, and semantic memory in adults, children, and neuropsychological populations. Numerous theories and models in these areas are based on representations and computations involving semantic features. Consequently, empirically derived semantic feature production norms have played, and continue to play, a highly useful role in these domains. This article describes a set of feature norms collected from approximately 725 participants for 541 living (dog) and nonliving (chair) basic-level concepts, the largest such set of norms developed to date. This article describes the norms and numerous statistics associated with them. Our aim is to make these norms available to facilitate other research, while obviating the need to repeat the labor-intensive methods involved in collecting and analyzing such norms. The full set of norms may be downloaded from www.psychonomic.org/archive.Semantic feature production norms for a large set ...[Behav Res Methods. 2005] - PubMed ResultHow To Break Anonymity of the Netflix Prize Datasethttps://puma.uni-kassel.de/bibtex/286b686a7fad55fa225123b2f79de87a8/hothohotho2007-12-14T09:04:20+01:00Preis anonymity dataset netflix prize recommender <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Arvind Narayanan" itemprop="url" href="/author/Arvind%20Narayanan"><span itemprop="name">A. Narayanan</span></a></span>, und <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Vitaly Shmatikov" itemprop="url" href="/author/Vitaly%20Shmatikov"><span itemprop="name">V. Shmatikov</span></a></span>. </span>(<em><span>2006<meta content="2006" itemprop="datePublished"/></span></em>)Fri Dec 14 09:04:20 CET 2007How To Break Anonymity of the Netflix Prize Dataset2006Preis anonymity dataset netflix prize recommender We present a new class of statistical de-anonymization attacks against high-dimensional micro-data, such as individual preferences, recommendations, transaction records and so on. Our techniques are robust to perturbation in the data and tolerate some mistakes in the adversary's background knowledge.[cs/0610105] How To Break Anonymity of the Netflix Prize DatasetUCI Repository of machine learning databaseshttps://puma.uni-kassel.de/bibtex/285308db3df761f63f16a7cab4eb8d4aa/hothohotho2006-06-23T07:06:19+02:00learning data dataset dm mining machine ml uci <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="C.L. Blake D.J. Newman" itemprop="url" href="/author/C.L.%20Blake%20D.J.%20Newman"><span itemprop="name">C. Newman</span></a></span>, und <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="C.J. Merz" itemprop="url" href="/author/C.J.%20Merz"><span itemprop="name">C. Merz</span></a></span>. </span>(<em><span>1998<meta content="1998" itemprop="datePublished"/></span></em>)Fri Jun 23 07:06:19 CEST 2006{UCI} Repository of machine learning databases1998learning data dataset dm mining machine ml uci UCI Machine Learning Repository