PUMA publications for /tag/clusteringhttps://puma.uni-kassel.de/tag/clusteringPUMA RSS feed for /tag/clustering2024-03-28T23:57:46+01:00Conceptual Clustering with Iceberg Concept Latticeshttps://puma.uni-kassel.de/bibtex/2f4ec21d5f63dbc213a3a6eae076c4b62/seboettgseboettg2015-11-12T13:25:22+01:002001 analysis closed clustering concept conceptual discovery fca formal iceberg itemsets kdd knowledge lattices <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="G. Stumme" itemprop="url" href="/author/G.%20Stumme"><span itemprop="name">G. Stumme</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="R. Taouil" itemprop="url" href="/author/R.%20Taouil"><span itemprop="name">R. Taouil</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Y. Bastide" itemprop="url" href="/author/Y.%20Bastide"><span itemprop="name">Y. Bastide</span></a></span>, und <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="L. Lakhal" itemprop="url" href="/author/L.%20Lakhal"><span itemprop="name">L. Lakhal</span></a></span>. </span><span itemtype="http://schema.org/Book" itemscope="itemscope" itemprop="isPartOf"><em><span itemprop="name">Proc. GI-Fachgruppentreffen Maschinelles Lernen (FGML'01)</span>, </em></span><em>Universität Dortmund 763, </em>(<em><span>Oktober 2001<meta content="Oktober 2001" itemprop="datePublished"/></span></em>)Thu Nov 12 13:25:22 CET 2015Universität Dortmund 763Proc. GI-Fachgruppentreffen Maschinelles Lernen (FGML'01)OctoberConceptual Clustering with Iceberg Concept Lattices20012001 analysis closed clustering concept conceptual discovery fca formal iceberg itemsets kdd knowledge lattices Publications of Gerd StummeStatistical Methods for Disease Clusteringhttps://puma.uni-kassel.de/bibtex/2aa3f9cbf0e4ff83c7323cb2f1d7422eb/dreisteindreistein2014-10-23T15:56:42+02:00bachelor clustering disease kursarbeit literaturliste <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Toshiro Tango" itemprop="url" href="/author/Toshiro%20Tango"><span itemprop="name">T. Tango</span></a></span>. </span><em>Statistics for Biology and Health </em><em><span itemprop="publisher">Springer New York</span>, </em><em>New York, NY, </em><em><span itemprop="bookEdition">1</span> Edition, </em>(<em><span>2010<meta content="2010" itemprop="datePublished"/></span></em>)Thu Oct 23 15:56:42 CEST 2014New York, NY1Statistics for Biology and HealthStatistical Methods for Disease Clustering2010bachelor clustering disease kursarbeit literaturliste The development of powerful computing environment and the geographical information system (GIS) in recent decades has thrust the analysis of geo-referenced disease incidence data into the mainstream of spatial epidemiology. This book offers a modern perspective on statistical methods for detecting disease clustering, an indispensable procedure to find a statistical evidence on aetiology of the disease under study.
With increasing public health concerns about environmental risks, the need for sophisticated methods for analyzing spatial health events is immediate. Furthermore, the research area of statistical methods for disease clustering now attracts a wide audience due to the perceived need to implement wide-ranging monitoring systems to detect possible health-related events such as the occurrence of the severe acute respiratory syndrome (SARS), pandemic influenza and bioterrorismBIRCH: An Efficient Data Clustering Method for Very Large Databaseshttps://puma.uni-kassel.de/bibtex/2250cecc10ceecd05a96bed00b6cf0fd7/stephandoerfelstephandoerfel2014-10-23T15:50:46+02:00birch clustering kdd <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Tian Zhang" itemprop="url" href="/author/Tian%20Zhang"><span itemprop="name">T. Zhang</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Raghu Ramakrishnan" itemprop="url" href="/author/Raghu%20Ramakrishnan"><span itemprop="name">R. Ramakrishnan</span></a></span>, und <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Miron Livny" itemprop="url" href="/author/Miron%20Livny"><span itemprop="name">M. Livny</span></a></span>. </span><span itemtype="http://schema.org/Book" itemscope="itemscope" itemprop="isPartOf"><em><span itemprop="name">Proceedings of the 1996 ACM SIGMOD International Conference on Management of Data</span>, </em></span><em>Seite <span itemprop="pagination">103--114</span>. </em><em>New York, NY, USA, </em><em><span itemprop="publisher">ACM</span>, </em>(<em><span>1996<meta content="1996" itemprop="datePublished"/></span></em>)Thu Oct 23 15:50:46 CEST 2014New York, NY, USAProceedings of the 1996 ACM SIGMOD International Conference on Management of Data103--114SIGMOD '96BIRCH: An Efficient Data Clustering Method for Very Large Databases1996birch clustering kdd BIRCHData Clustering: A Reviewhttps://puma.uni-kassel.de/bibtex/2bd7234f7139a1651acfaed57b5c2551f/hothohotho2014-02-05T14:32:51+01:00clustering overview review <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="A. K. Jain" itemprop="url" href="/author/A.%20K.%20Jain"><span itemprop="name">A. Jain</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="M. N. Murty" itemprop="url" href="/author/M.%20N.%20Murty"><span itemprop="name">M. Murty</span></a></span>, und <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="P. J. Flynn" itemprop="url" href="/author/P.%20J.%20Flynn"><span itemprop="name">P. Flynn</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 Comput. Surv.</em></span></span> <em><span itemtype="http://schema.org/PublicationVolume" itemscope="itemscope" itemprop="isPartOf"><span itemprop="volumeNumber">31 </span></span>(<span itemprop="issueNumber">3</span>):
<span itemprop="pagination">264--323</span></em> </span>(<em><span>September 1999<meta content="September 1999" itemprop="datePublished"/></span></em>)Wed Feb 05 14:32:51 CET 2014New York, NY, USAACM Comput. Surv.sep3264--323Data Clustering: A Review311999clustering overview review Clustering is the unsupervised classification of patterns (observations, data items, or feature vectors) into groups (clusters). The clustering problem has been addressed in many contexts and by researchers in many disciplines; this reflects its broad appeal and usefulness as one of the steps in exploratory data analysis. However, clustering is a difficult problem combinatorially, and differences in assumptions and contexts in different communities has made the transfer of useful generic concepts and methodologies slow to occur. This paper presents an overviewof pattern clustering methods from a statistical pattern recognition perspective, with a goal of providing useful advice and references to fundamental concepts accessible to the broad community of clustering practitioners. We present a taxonomy of clustering techniques, and identify cross-cutting themes and recent advances. We also describe some important applications of clustering algorithms such as image segmentation, object recognition, and information retrieval.Data clusteringData Clustering: Algorithms and Applicationshttps://puma.uni-kassel.de/bibtex/27f1541e5800e6c36c67dd6bc0ef64ba7/hothohotho2013-10-27T15:22:54+01:00clustering toread <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="editor"><a title="Charu C. Aggarwal" itemprop="url" href="/author/Charu%20C.%20Aggarwal"><span itemprop="name">C. Aggarwal</span></a></span>, und <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="editor"><a title="Chandan K. Reddy" itemprop="url" href="/author/Chandan%20K.%20Reddy"><span itemprop="name">C. Reddy</span></a></span> (Hrsg.).
. </span><em><span itemprop="publisher">CRC Press</span>, </em>(<em><span>2014<meta content="2014" itemprop="datePublished"/></span></em>)Sun Oct 27 15:22:54 CET 2013Data Clustering: Algorithms and Applications2014clustering toread dblp: books/crc/aggarwal2013Data clustering: a reviewhttps://puma.uni-kassel.de/bibtex/2b19bcef82a04eb82ee4abde53ee7d1c2/hothohotho2012-09-05T11:30:26+02:00clustering <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="A. K. Jain" itemprop="url" href="/author/A.%20K.%20Jain"><span itemprop="name">A. Jain</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="M. N. Murty" itemprop="url" href="/author/M.%20N.%20Murty"><span itemprop="name">M. Murty</span></a></span>, und <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="P. J. Flynn" itemprop="url" href="/author/P.%20J.%20Flynn"><span itemprop="name">P. Flynn</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 Comput. Surv.</em></span></span> <em><span itemtype="http://schema.org/PublicationVolume" itemscope="itemscope" itemprop="isPartOf"><span itemprop="volumeNumber">31 </span></span>(<span itemprop="issueNumber">3</span>):
<span itemprop="pagination">264--323</span></em> </span>(<em><span>September 1999<meta content="September 1999" itemprop="datePublished"/></span></em>)Wed Sep 05 11:30:26 CEST 2012New York, NY, USAACM Comput. Surv.sep3264--323Data clustering: a review311999clustering Data clusteringClass visualization of high-dimensional data with applicationshttps://puma.uni-kassel.de/bibtex/203e92f40796a0093a6e882a83f5cd995/hothohotho2012-07-13T14:30:28+02:00alphaworks cluster clustering dm visualization <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Inderjit S. Dhillon" itemprop="url" href="/author/Inderjit%20S.%20Dhillon"><span itemprop="name">I. Dhillon</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Dharmendra S. Modha" itemprop="url" href="/author/Dharmendra%20S.%20Modha"><span itemprop="name">D. Modha</span></a></span>, und <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="W. Scott Spangler" itemprop="url" href="/author/W.%20Scott%20Spangler"><span itemprop="name">W. Spangler</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>Computational Statistics & Data Analysis</em></span></span> <em><span itemtype="http://schema.org/PublicationVolume" itemscope="itemscope" itemprop="isPartOf"><span itemprop="volumeNumber">41 </span></span>(<span itemprop="issueNumber">1</span>):
<span itemprop="pagination">59-90</span></em> </span>(<em><span>November 2002<meta content="November 2002" itemprop="datePublished"/></span></em>)Fri Jul 13 14:30:28 CEST 2012Computational Statistics \& Data AnalysisNovember159-90Class visualization of high-dimensional data with applications412002alphaworks cluster clustering dm visualization No abstract is available for this item.Intelligent text categorization and clusteringhttps://puma.uni-kassel.de/bibtex/21a61a34d4984ee4451be75902c25c49b/hothohotho2012-05-07T16:08:44+02:00clustering <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Nadia Nedjah" itemprop="url" href="/author/Nadia%20Nedjah"><span itemprop="name">N. Nedjah</span></a></span>. </span>(<em><span>2009<meta content="2009" itemprop="datePublished"/></span></em>)Mon May 07 16:08:44 CEST 2012BerlinIntelligent text categorization and clustering2009clustering "Automatic Text Categorization and Clustering are becoming more and more important as the amount of text in electronic format grows and the access to it becomes more necessary and widespread. Well known applications are spam filtering and web search, but a large number of everyday uses exists (intelligent web search, data mining, law enforcement, etc.). Currently, researchers are employing many intelligent techniques for text categorization and clustering, ranging from support vector machines and neural networks to Bayesian inference and algebraic methods, such as Latent Semantic Indexing." "This volume offers a wide spectrum of research work developed for intelligent text categorization and clustering."--Jacket.Intelligent Text Categorization and ClusteringOn Finding Graph Clusterings with Maximum Modularityhttps://puma.uni-kassel.de/bibtex/26fd10991ee4e3880c64c11862884ead7/stephandoerfelstephandoerfel2012-01-18T15:45:34+01:00clustering graph modularity theory <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Ulrik Brandes" itemprop="url" href="/author/Ulrik%20Brandes"><span itemprop="name">U. Brandes</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Daniel Delling" itemprop="url" href="/author/Daniel%20Delling"><span itemprop="name">D. Delling</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Marco Gaertler" itemprop="url" href="/author/Marco%20Gaertler"><span itemprop="name">M. Gaertler</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Robert Görke" itemprop="url" href="/author/Robert%20G%c3%b6rke"><span itemprop="name">R. Görke</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Martin Hoefer" itemprop="url" href="/author/Martin%20Hoefer"><span itemprop="name">M. Hoefer</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Zoran Nikoloski" itemprop="url" href="/author/Zoran%20Nikoloski"><span itemprop="name">Z. Nikoloski</span></a></span>, und <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Dorothea Wagner" itemprop="url" href="/author/Dorothea%20Wagner"><span itemprop="name">D. Wagner</span></a></span>. </span><span itemtype="http://schema.org/Book" itemscope="itemscope" itemprop="isPartOf"><em><span itemprop="name">Graph-Theoretic Concepts in Computer Science</span>, </em><em>Volume 4769 von Lecture Notes in Computer Science, </em><em><span itemprop="publisher">Springer</span>, </em><em>Berlin / Heidelberg, </em></span>(<em><span>2007<meta content="2007" itemprop="datePublished"/></span></em>)Wed Jan 18 15:45:34 CET 2012Berlin / HeidelbergGraph-Theoretic Concepts in Computer Science121-132Lecture Notes in Computer ScienceOn Finding Graph Clusterings with Maximum Modularity47692007clustering graph modularity theory Modularity is a recently introduced quality measure for graph clusterings. It has immediately received considerable attention in several disciplines, and in particular in the complex systems literature, although its properties are not well understood. We study the problem of finding clusterings with maximum modularity, thus providing theoretical foundations for past and present work based on this measure. More precisely, we prove the conjectured hardness of maximizing modularity both in the general case and with the restriction to cuts, and give an Integer Linear Programming formulation. This is complemented by first insights into the behavior and performance of the commonly applied greedy agglomaration approach.Abstract - SpringerLinkModularity and community structure in networkshttps://puma.uni-kassel.de/bibtex/25dd9d0c2155f242393e63547d8a2347f/jaeschkejaeschke2011-12-21T08:52:42+01:00clustering community graph modularity network structure <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="M. E. J. Newman" itemprop="url" href="/author/M.%20E.%20J.%20Newman"><span itemprop="name">M. Newman</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>Proceedings of the National Academy of Sciences</em></span></span> <em><span itemtype="http://schema.org/PublicationVolume" itemscope="itemscope" itemprop="isPartOf"><span itemprop="volumeNumber">103 </span></span>(<span itemprop="issueNumber">23</span>):
<span itemprop="pagination">8577--8582</span></em> </span>(<em><span>2006<meta content="2006" itemprop="datePublished"/></span></em>)Wed Dec 21 08:52:42 CET 2011Proceedings of the National Academy of Sciences238577--8582Modularity and community structure in networks1032006clustering community graph modularity network structure Many networks of interest in the sciences, including social networks, computer networks, and metabolic and regulatory networks, are found to divide naturally into communities or modules. The problem of detecting and characterizing this community structure is one of the outstanding issues in the study of networked systems. One highly effective approach is the optimization of the quality function known as “modularity” over the possible divisions of a network. Here I show that the modularity can be expressed in terms of the eigenvectors of a characteristic matrix for the network, which I call the modularity matrix, and that this expression leads to a spectral algorithm for community detection that returns results of demonstrably higher quality than competing methods in shorter running times. I illustrate the method with applications to several published network data sets.Content Aggregation on Knowledge Bases using Graph Clusteringhttps://puma.uni-kassel.de/bibtex/21788c88e04112a4491f19dfffb8dc39e/itegiteg2011-11-22T10:26:32+01:002006 aggregation clustering content graph itegpub l3s myown nepomuk ontologies ontology seminar2006 theory <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Christoph Schmitz" itemprop="url" href="/author/Christoph%20Schmitz"><span itemprop="name">C. Schmitz</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Andreas Hotho" itemprop="url" href="/author/Andreas%20Hotho"><span itemprop="name">A. Hotho</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Robert Jäschke" itemprop="url" href="/author/Robert%20J%c3%a4schke"><span itemprop="name">R. Jäschke</span></a></span>, und <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Gerd Stumme" itemprop="url" href="/author/Gerd%20Stumme"><span itemprop="name">G. Stumme</span></a></span>. </span><span itemtype="http://schema.org/Book" itemscope="itemscope" itemprop="isPartOf"><em><span itemprop="name">The Semantic Web: Research and Applications</span>, </em></span><em>Volume 4011 von LNAI, </em><em>Seite <span itemprop="pagination">530-544</span>. </em><em>Heidelberg, </em><em><span itemprop="publisher">Springer</span>, </em>(<em><span>2006<meta content="2006" itemprop="datePublished"/></span></em>)Tue Nov 22 10:26:32 CET 2011HeidelbergThe Semantic Web: Research and Applications530-544LNAIContent Aggregation on Knowledge Bases using Graph Clustering401120062006 aggregation clustering content graph itegpub l3s myown nepomuk ontologies ontology seminar2006 theory Recently, research projects such as PADLR and SWAP
have developed tools like Edutella or Bibster, which are targeted at
establishing peer-to-peer knowledge management (P2PKM) systems. In
such a system, it is necessary to obtain provide brief semantic
descriptions of peers, so that routing algorithms or matchmaking
processes can make decisions about which communities peers should
belong to, or to which peers a given query should be forwarded.
This paper provides a graph clustering technique on
knowledge bases for that purpose. Using this clustering, we can show
that our strategy requires up to 58% fewer queries than the
baselines to yield full recall in a bibliographic P2PKM scenario.Conceptual Clustering of Social Bookmark Siteshttps://puma.uni-kassel.de/bibtex/26d5188d66564fe4ed7386e28868504de/itegiteg2011-11-22T10:26:32+01:002007 Social bookmark bookmarking clustering collaborative conceptual folksonomies folksonomy itegpub myown social tagging tagorapub <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Miranda Grahl" itemprop="url" href="/author/Miranda%20Grahl"><span itemprop="name">M. Grahl</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Andreas Hotho" itemprop="url" href="/author/Andreas%20Hotho"><span itemprop="name">A. Hotho</span></a></span>, und <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Gerd Stumme" itemprop="url" href="/author/Gerd%20Stumme"><span itemprop="name">G. Stumme</span></a></span>. </span><span itemtype="http://schema.org/Book" itemscope="itemscope" itemprop="isPartOf"><em><span itemprop="name">Workshop Proceedings of Lernen -- Wissensentdeckung -- Adaptivität (LWA 2007)</span>, </em></span><em>Seite <span itemprop="pagination">50-54</span>. </em><em><span itemprop="publisher">Martin-Luther-Universität Halle-Wittenberg</span>, </em>(<em><span>September 2007<meta content="September 2007" itemprop="datePublished"/></span></em>)Tue Nov 22 10:26:32 CET 2011Workshop Proceedings of Lernen -- Wissensentdeckung -- Adaptivität (LWA 2007)sep50-54Conceptual Clustering of Social Bookmark Sites20072007 Social bookmark bookmarking clustering collaborative conceptual folksonomies folksonomy itegpub myown social tagging tagorapub Do German physicians want electronic health services? A characterization of potential adopters and rejectors in German ambulatory carehttps://puma.uni-kassel.de/bibtex/26d8d3744dda9624c4ae1b10fed7b2e3e/itegiteg2011-11-22T10:26:32+01:00Adoption Ambulatory Care Clustering Data Electronic Equipment Health Infrastructure Practice Security Services Standardization Technology Telematics itegpub myown pub_jml <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="S. Duennebeil" itemprop="url" href="/author/S.%20Duennebeil"><span itemprop="name">S. Duennebeil</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="A. Sunyaev" itemprop="url" href="/author/A.%20Sunyaev"><span itemprop="name">A. Sunyaev</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="I. Blohm" itemprop="url" href="/author/I.%20Blohm"><span itemprop="name">I. Blohm</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="J. M. Leimeister" itemprop="url" href="/author/J.%20M.%20Leimeister"><span itemprop="name">J. Leimeister</span></a></span>, und <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="H. Krcmar" itemprop="url" href="/author/H.%20Krcmar"><span itemprop="name">H. Krcmar</span></a></span>. </span><span itemtype="http://schema.org/Book" itemscope="itemscope" itemprop="isPartOf"><em><span itemprop="name">3. International Conference on Health Informatics (HealthInf) 2010</span>, </em></span><em>Valencia, Spain, </em>(<em><span>2010<meta content="2010" itemprop="datePublished"/></span></em>)<em>163 (11-10).</em>Tue Nov 22 10:26:32 CET 2011Valencia, Spain3. International Conference on Health Informatics (HealthInf) 2010163 (11-10)Do German physicians want electronic health services? A characterization of potential adopters and rejectors in German ambulatory care2010Adoption Ambulatory Care Clustering Data Electronic Equipment Health Infrastructure Practice Security Services Standardization Technology Telematics itegpub myown pub_jml Exploit the tripartite network of social tagging for web clusteringhttps://puma.uni-kassel.de/bibtex/286160cf68758ec60922323a34a7833f0/benzbenz2011-11-21T10:50:36+01:00clustering web bachelor:2011:bachmann <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Caimei Lu" itemprop="url" href="/author/Caimei%20Lu"><span itemprop="name">C. Lu</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Xin Chen" itemprop="url" href="/author/Xin%20Chen"><span itemprop="name">X. Chen</span></a></span>, und <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="E. K. Park" itemprop="url" href="/author/E.%20K.%20Park"><span itemprop="name">E. Park</span></a></span>. </span><span itemtype="http://schema.org/Book" itemscope="itemscope" itemprop="isPartOf"><em><span itemprop="name">Proceeding of the 18th ACM conference on Information and knowledge management</span>, </em></span><em>Seite <span itemprop="pagination">1545--1548</span>. </em><em>New York, NY, USA, </em><em><span itemprop="publisher">ACM</span>, </em>(<em><span>2009<meta content="2009" itemprop="datePublished"/></span></em>)Mon Nov 21 10:50:36 CET 2011New York, NY, USAProceeding of the 18th ACM conference on Information and knowledge management1545--1548CIKM '09Exploit the tripartite network of social tagging for web clustering2009clustering web bachelor:2011:bachmann In this poster, we investigate how to enhance web clustering by leveraging the tripartite network of social tagging systems. We propose a clustering method, called "Tripartite Clustering", which cluster the three types of nodes (resources, users and tags) simultaneously based on the links in the social tagging network. The proposed method is experimented on a real-world social tagging dataset sampled from del.icio.us. We also compare the proposed clustering approach with K-means. All the clustering results are evaluated against a human-maintained web directory. The experimental results show that Tripartite Clustering significantly outperforms the content-based K-means approach and achieves performance close to that of social annotation-based K-means whereas generating much more useful information.A survey of Web clustering engineshttps://puma.uni-kassel.de/bibtex/21921bab51019d89a0b740c43d8aafd23/benzbenz2011-11-21T10:49:46+01:00clustering web bachelor:2011:bachmann <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Claudio Carpineto" itemprop="url" href="/author/Claudio%20Carpineto"><span itemprop="name">C. Carpineto</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Stanislaw Osiński" itemprop="url" href="/author/Stanislaw%20Osi%5c'%7bn%7dski"><span itemprop="name">S. Osiński</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Giovanni Romano" itemprop="url" href="/author/Giovanni%20Romano"><span itemprop="name">G. Romano</span></a></span>, und <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Dawid Weiss" itemprop="url" href="/author/Dawid%20Weiss"><span itemprop="name">D. Weiss</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 Comput. Surv.</em></span></span> </span>(<em><span>Juli 2009<meta content="Juli 2009" itemprop="datePublished"/></span></em>)Mon Nov 21 10:49:46 CET 2011New York, NY, USAACM Comput. Surv.July17:1--17:38A survey of Web clustering engines412009clustering web bachelor:2011:bachmann Web clustering engines organize search results by topic, thus offering a complementary view to the flat-ranked list returned by conventional search engines. In this survey, we discuss the issues that must be addressed in the development of a Web clustering engine, including acquisition and preprocessing of search results, their clustering and visualization. Search results clustering, the core of the system, has specific requirements that cannot be addressed by classical clustering algorithms. We emphasize the role played by the quality of the cluster labels as opposed to optimizing only the clustering structure. We highlight the main characteristics of a number of existing Web clustering engines and also discuss how to evaluate their retrieval performance. Some directions for future research are finally presented.Validation of Text Clustering Based on Document Contents.https://puma.uni-kassel.de/bibtex/22f23db9219b4d693acf15d7401684499/hothohotho2011-11-02T11:22:18+01:00clustering text toread <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Jarmo Toivonen" itemprop="url" href="/author/Jarmo%20Toivonen"><span itemprop="name">J. Toivonen</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Ari Visa" itemprop="url" href="/author/Ari%20Visa"><span itemprop="name">A. Visa</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Tomi Vesanen" itemprop="url" href="/author/Tomi%20Vesanen"><span itemprop="name">T. Vesanen</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Barbro Back" itemprop="url" href="/author/Barbro%20Back"><span itemprop="name">B. Back</span></a></span>, und <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Hannu Vanharanta" itemprop="url" href="/author/Hannu%20Vanharanta"><span itemprop="name">H. Vanharanta</span></a></span>. </span><span itemtype="http://schema.org/Book" itemscope="itemscope" itemprop="isPartOf"><em><span itemprop="name">MLDM</span>, </em></span><em>Volume 2123 von Lecture Notes in Computer Science, </em><em>Seite <span itemprop="pagination">184-195</span>. </em><em><span itemprop="publisher">Springer</span>, </em>(<em><span>2001<meta content="2001" itemprop="datePublished"/></span></em>)Wed Nov 02 11:22:18 CET 2011MLDMconf/mldm/2001184-195Lecture Notes in Computer ScienceValidation of Text Clustering Based on Document Contents.21232001clustering text toread Modeling the clustering in citation networkshttps://puma.uni-kassel.de/bibtex/2d668e639ed78f4c7ec53eeba64d8ae2a/stephandoerfelstephandoerfel2011-08-12T12:35:08+02:00citation clustering info20 network <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Fu-Xin Ren" itemprop="url" href="/author/Fu-Xin%20Ren"><span itemprop="name">F. Ren</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Xue-Qi Cheng" itemprop="url" href="/author/Xue-Qi%20Cheng"><span itemprop="name">X. Cheng</span></a></span>, und <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Hua-Wei Shen" itemprop="url" href="/author/Hua-Wei%20Shen"><span itemprop="name">H. Shen</span></a></span>. </span>(<em><span>2011<meta content="2011" itemprop="datePublished"/></span></em>)<em>cite arxiv:1104.4209.</em>Fri Aug 12 12:35:08 CEST 2011cite arxiv:1104.4209Modeling the clustering in citation networks2011citation clustering info20 network It has been known for a long time that citation networks are always highly clustered, such as the existences of abundant triangles and high clustering coefficient. In a growth model, one typical way to produce clustering is using the trid formation mechanism. However, we find that this mechanism fails to generate enough triangles in a real-world citation network. By analyzing the network, it is found that one paper always cites papers that are already highly connected. We point out that the highly connected papers may refer to similar research topic and one subsequent paper tends to cite all of them. Based on this assumption, we propose a growth model for citation networks in which a new paper i firstly attaches to one relevant paper j and then with a probability links those papers in the same clique to which j belongs. We compare our model to two real-world citation networks - one on a special research area and the other on multidisciplinary sciences. Results show that for the two networks the in-degree distributions are matched and the clustering features, i.e., the number of triangles and the average clustering coefficient, are well reproduced. [1104.4209] Modeling the clustering in citation networksPersonalized Hierarchical Structuringhttps://puma.uni-kassel.de/bibtex/247cf055e43db23e10fbf5bb0a446d730/benzbenz2011-07-29T09:40:37+02:00clustering hierarchical <meta content="thesis" itemprop="educationalUse"/><span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Korinna Bade" itemprop="url" href="/author/Korinna%20Bade"><span itemprop="name">K. Bade</span></a></span>. </span><em>Otto-von-Guericke-Universitat Magdeburg, </em>(<em><span>2009<meta content="2009" itemprop="datePublished"/></span></em>)Fri Jul 29 09:40:37 CEST 2011Personalized Hierarchical Structuring2009clustering hierarchical Improving the Exploration of Tag Spaces Using Automated Tag Clusteringhttps://puma.uni-kassel.de/bibtex/277bc7f7e46481b47c11dd9e53d5741e0/benzbenz2011-07-07T23:23:07+02:00clustering tag toread <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Joni Radelaar" itemprop="url" href="/author/Joni%20Radelaar"><span itemprop="name">J. Radelaar</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Aart-Jan Boor" itemprop="url" href="/author/Aart-Jan%20Boor"><span itemprop="name">A. Boor</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Damir Vandic" itemprop="url" href="/author/Damir%20Vandic"><span itemprop="name">D. Vandic</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Jan-Willem van Dam" itemprop="url" href="/author/Jan-Willem%20van%20Dam"><span itemprop="name">J. van Dam</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Frederik Hogenboom" itemprop="url" href="/author/Frederik%20Hogenboom"><span itemprop="name">F. Hogenboom</span></a></span>, und <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Flavius Frasincar" itemprop="url" href="/author/Flavius%20Frasincar"><span itemprop="name">F. Frasincar</span></a></span>. </span><span itemtype="http://schema.org/Book" itemscope="itemscope" itemprop="isPartOf"><em><span itemprop="name">Web Engineering</span>, </em><em>Volume 6757 von Lecture Notes in Computer Science, </em><em><span itemprop="publisher">Springer Berlin / Heidelberg</span>, </em></span><em>10.1007/978-3-642-22233-7_19.</em>(<em><span>2011<meta content="2011" itemprop="datePublished"/></span></em>)Thu Jul 07 23:23:07 CEST 2011Web Engineering10.1007/978-3-642-22233-7_19274-288Lecture Notes in Computer ScienceImproving the Exploration of Tag Spaces Using Automated Tag Clustering67572011clustering tag toread Community Structure in Directed Networkshttps://puma.uni-kassel.de/bibtex/293726cc0540f75ee1cb515b2923d69e8/stephandoerfelstephandoerfel2011-03-29T12:10:59+02:002011 clustering community directed modularity networks <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="E. A. Leicht" itemprop="url" href="/author/E.%20A.%20Leicht"><span itemprop="name">E. Leicht</span></a></span>, und <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="M. E. J. Newman" itemprop="url" href="/author/M.%20E.%20J.%20Newman"><span itemprop="name">M. Newman</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>Phys. Rev. Lett.</em></span></span> <em><span itemtype="http://schema.org/PublicationVolume" itemscope="itemscope" itemprop="isPartOf"><span itemprop="volumeNumber">100 </span></span>(<span itemprop="issueNumber">11</span>):
<span itemprop="pagination">118703</span></em> </span>(<em><span>März 2008<meta content="März 2008" itemprop="datePublished"/></span></em>)Tue Mar 29 12:10:59 CEST 2011Phys. Rev. Lett.mar11118703Community Structure in Directed Networks10020082011 clustering community directed modularity networks