PUMA publications for /user/hotho/evaluationhttps://puma.uni-kassel.de/user/hotho/evaluationPUMA RSS feed for /user/hotho/evaluation2024-03-19T10:30:07+01:00Community Assessment using Evidence Networkshttps://puma.uni-kassel.de/bibtex/20f45e870093c053e6f41f54c14bda46b/hothohotho2011-11-30T14:49:15+01:002011 community evaluation knowledge mining myown <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Folke Mitzlaff" itemprop="url" href="/author/Folke%20Mitzlaff"><span itemprop="name">F. Mitzlaff</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Martin Atzmueller" itemprop="url" href="/author/Martin%20Atzmueller"><span itemprop="name">M. Atzmueller</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Dominik Benz" itemprop="url" href="/author/Dominik%20Benz"><span itemprop="name">D. Benz</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">Analysis of Social Media and Ubiquitous Data</span>, </em></span><em>Volume 6904 von LNAI, </em>(<em><span>2011<meta content="2011" itemprop="datePublished"/></span></em>)Wed Nov 30 14:49:15 CET 2011Analysis of Social Media and Ubiquitous DataLNAI{Community Assessment using Evidence Networks}690420112011 community evaluation knowledge mining myown A Survey of Accuracy Evaluation Metrics of Recommendation Taskshttps://puma.uni-kassel.de/bibtex/249600df05a884106989d71dedcaa7e1b/hothohotho2011-02-09T15:32:02+01:00evaluation metrics recommender survey toread <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Asela Gunawardana" itemprop="url" href="/author/Asela%20Gunawardana"><span itemprop="name">A. Gunawardana</span></a></span>, und <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Guy Shani" itemprop="url" href="/author/Guy%20Shani"><span itemprop="name">G. Shani</span></a></span>. </span><em>Volume v10 von 2935, </em>(<em><span>2009<meta content="2009" itemprop="datePublished"/></span></em>)Wed Feb 09 15:32:02 CET 20112935 A Survey of Accuracy Evaluation Metrics of Recommendation Tasks v102009evaluation metrics recommender survey toread Empirical Comparison of Algorithms for Network Community Detectionhttps://puma.uni-kassel.de/bibtex/2410a9cbea51ea5dd3c56aad26a0e11b2/hothohotho2010-04-28T20:43:59+02:00clustering community comparision empirical evaluation graph toread <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Jure Leskovec" itemprop="url" href="/author/Jure%20Leskovec"><span itemprop="name">J. Leskovec</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Kevin J. Lang" itemprop="url" href="/author/Kevin%20J.%20Lang"><span itemprop="name">K. Lang</span></a></span>, und <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Michael W. Mahoney" itemprop="url" href="/author/Michael%20W.%20Mahoney"><span itemprop="name">M. Mahoney</span></a></span>. </span>(<em><span>2010<meta content="2010" itemprop="datePublished"/></span></em>)<em>cite arxiv:1004.3539.</em>Wed Apr 28 20:43:59 CEST 2010cite arxiv:1004.3539
Empirical Comparison of Algorithms for Network Community Detection2010clustering community comparision empirical evaluation graph toread Detecting clusters or communities in large real-world graphs such as large
social or information networks is a problem of considerable interest. In
practice, one typically chooses an objective function that captures the
intuition of a network cluster as set of nodes with better internal
connectivity than external connectivity, and then one applies approximation
algorithms or heuristics to extract sets of nodes that are related to the
objective function and that "look like" good communities for the application of
interest. In this paper, we explore a range of network community detection
methods in order to compare them and to understand their relative performance
and the systematic biases in the clusters they identify. We evaluate several
common objective functions that are used to formalize the notion of a network
community, and we examine several different classes of approximation algorithms
that aim to optimize such objective functions. In addition, rather than simply
fixing an objective and asking for an approximation to the best cluster of any
size, we consider a size-resolved version of the optimization problem.
Considering community quality as a function of its size provides a much finer
lens with which to examine community detection algorithms, since objective
functions and approximation algorithms often have non-obvious size-dependent
behavior.
Empirical Comparison of Algorithms for Network Community DetectionA Survey of Ontology Evaluation Techniqueshttps://puma.uni-kassel.de/bibtex/28c910a2d3f6708b23e03e06ff843c8a8/hothohotho2010-02-11T10:53:26+01:00evaluation ol ontology survey <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Janez Brank" itemprop="url" href="/author/Janez%20Brank"><span itemprop="name">J. Brank</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Marko Grobelnik" itemprop="url" href="/author/Marko%20Grobelnik"><span itemprop="name">M. Grobelnik</span></a></span>, und <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Dunja Mladenić" itemprop="url" href="/author/Dunja%20Mladeni%7b%5c'c%7d"><span itemprop="name">D. Mladenić</span></a></span>. </span><span itemtype="http://schema.org/Book" itemscope="itemscope" itemprop="isPartOf"><em><span itemprop="name">Proc. of 8th Int. multi-conf. Information Society</span>, </em></span><em>Seite <span itemprop="pagination">166--169</span>. </em>(<em><span>2005<meta content="2005" itemprop="datePublished"/></span></em>)Thu Feb 11 10:53:26 CET 2010Proc. of 8th Int. multi-conf. Information Society166--169A Survey of Ontology Evaluation Techniques2005evaluation ol ontology survey A nice survey of ontology evaluation methods, easy to read.AEON - An approach to the automatic evaluation of ontologieshttps://puma.uni-kassel.de/bibtex/2f8f0bb3e3495e7627770b470d1a5f1a3/hothohotho2008-12-19T09:19:20+01:002008 automatic evaluation ml myown ontology sw <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Johanna Völker" itemprop="url" href="/author/Johanna%20V%c3%b6lker"><span itemprop="name">J. Völker</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Denny Vrandečić" itemprop="url" href="/author/Denny%20Vrande%c4%8di%c4%87"><span itemprop="name">D. Vrandečić</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="York Sure" itemprop="url" href="/author/York%20Sure"><span itemprop="name">Y. Sure</span></a></span>, und <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><span itemtype="http://schema.org/PublicationIssue" itemscope="itemscope" itemprop="isPartOf"><span itemtype="http://schema.org/Periodical" itemscope="itemscope" itemprop="isPartOf"><span itemprop="name"><em>Applied Ontology</em></span></span> <em><span itemtype="http://schema.org/PublicationVolume" itemscope="itemscope" itemprop="isPartOf"><span itemprop="volumeNumber">3 </span></span>(<span itemprop="issueNumber">1-2</span>):
<span itemprop="pagination">41--62</span></em> </span>(<em><span>2008<meta content="2008" itemprop="datePublished"/></span></em>)Fri Dec 19 09:19:20 CET 2008Amsterdam, The Netherlands, The NetherlandsApplied Ontology1-241--62AEON - An approach to the automatic evaluation of ontologies320082008 automatic evaluation ml myown ontology sw OntoClean is an approach towards the formal evaluation of taxonomic relations in ontologies. The application of OntoClean consists of two main steps. First, concepts are tagged according to meta-properties known as rigidity, unity, dependency and identity. Second, the tagged concepts are checked according to predefined constraints to discover taxonomic errors. Although OntoClean is well documented in numerous publications, it is still used rather infrequently due to the high costs of application. Especially, the manual tagging of concepts with the correct meta-properties requires substantial efforts of highly experienced ontology engineers. In order to facilitate the use of OntoClean and to enable the evaluation of real-world ontologies, we provide AEON, a tool which automatically tags concepts with appropriate OntoClean meta-properties and performs the constraint checking. We use the Web as an embodiment of world knowledge, where we search for patterns that indicate how to properly tag concepts. We thoroughly evaluated our approach against a manually created gold standard. The evaluation shows the competitiveness of our approach while at the same time significantly lowering the costs. All of our results, i.e. the tool AEON as well as the experiment data, are publicly available.AEON - An approach to the automatic evaluation of ontologiesEvaluating tagging behavior in social bookmarking systems: metrics and design heuristicshttps://puma.uni-kassel.de/bibtex/25d0b61727d81aed019ba4297090108ca/hothohotho2008-12-01T15:06:53+01:00collaboration evaluation social tagging taggingsurvey toread <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Umer Farooq" itemprop="url" href="/author/Umer%20Farooq"><span itemprop="name">U. Farooq</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Thomas G. Kannampallil" itemprop="url" href="/author/Thomas%20G.%20Kannampallil"><span itemprop="name">T. Kannampallil</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="Craig H. Ganoe" itemprop="url" href="/author/Craig%20H.%20Ganoe"><span itemprop="name">C. Ganoe</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="John M. Carroll" itemprop="url" href="/author/John%20M.%20Carroll"><span itemprop="name">J. Carroll</span></a></span>, und <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Lee Giles" itemprop="url" href="/author/Lee%20Giles"><span itemprop="name">L. Giles</span></a></span>. </span><span itemtype="http://schema.org/Book" itemscope="itemscope" itemprop="isPartOf"><em><span itemprop="name">GROUP '07: Proceedings of the 2007 international ACM conference on Conference on supporting group work</span>, </em></span><em>Seite <span itemprop="pagination">351--360</span>. </em><em>New York, NY, USA, </em><em><span itemprop="publisher">ACM</span>, </em>(<em><span>2007<meta content="2007" itemprop="datePublished"/></span></em>)Mon Dec 01 15:06:53 CET 2008New York, NY, USAGROUP '07: Proceedings of the 2007 international ACM conference on Conference on supporting group work351--360Evaluating tagging behavior in social bookmarking systems: metrics and design heuristics2007collaboration evaluation social tagging taggingsurvey toread Evaluating tagging behavior in social bookmarking systemsImproving Text Classification by Using Encyclopedia Knowledgehttps://puma.uni-kassel.de/bibtex/266058efbca5abd1222f72c32365d23fa/hothohotho2008-08-25T15:40:01+02:00**** classification evaluation learning ol ontology text wikipedia <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Pu Wang" itemprop="url" href="/author/Pu%20Wang"><span itemprop="name">P. Wang</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Jian Hu" itemprop="url" href="/author/Jian%20Hu"><span itemprop="name">J. Hu</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Hua-Jun Zeng" itemprop="url" href="/author/Hua-Jun%20Zeng"><span itemprop="name">H. Zeng</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Lijun Chen" itemprop="url" href="/author/Lijun%20Chen"><span itemprop="name">L. Chen</span></a></span>, und <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Zheng Chen" itemprop="url" href="/author/Zheng%20Chen"><span itemprop="name">Z. Chen</span></a></span>. </span><span itemtype="http://schema.org/Book" itemscope="itemscope" itemprop="isPartOf"><em><span itemprop="name">Data Mining, 2007. ICDM 2007. Seventh IEEE International Conference on</span>, </em></span><em>Seite <span itemprop="pagination">332-341</span>. </em>(<em><span>2007<meta content="2007" itemprop="datePublished"/></span></em>)Mon Aug 25 15:40:01 CEST 2008Data Mining, 2007. ICDM 2007. Seventh IEEE International Conference on332-341Improving Text Classification by Using Encyclopedia Knowledge2007**** classification evaluation learning ol ontology text wikipedia The exponential growth of text documents available on the Internet has created an urgent need for accurate, fast, and general purpose text classification algorithms. However, the "bag of words" representation used for these classification methods is often unsatisfactory as it ignores relationships between important terms that do not co-occur literally. In order to deal with this problem, we integrate background knowledge - in our application: Wikipedia - into the process of classifying text documents. The experimental evaluation on Reuters newsfeeds and several other corpus shows that our classification results with encyclopedia knowledge are much better than the baseline "bag of words " methods.Welcome to IEEE Xplore 2.0: Improving Text Classification by Using Encyclopedia KnowledgeROC Graphs: Notes and Practical Considerations for Researchershttps://puma.uni-kassel.de/bibtex/2c580a50d58db5cd78d7dc5ab3cbd2a29/hothohotho2008-08-15T14:50:10+02:00auc evaluation roc tutorial <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="T. Fawcett" itemprop="url" href="/author/T.%20Fawcett"><span itemprop="name">T. Fawcett</span></a></span>. </span><em><span itemprop="producer">HP Laboratories</span>, </em>(<em><span>2004<meta content="2004" itemprop="datePublished"/></span></em>)Fri Aug 15 14:50:10 CEST 2008Tech Report HPL-2003-4ROC Graphs: Notes and Practical Considerations for Researchers2004auc evaluation roc tutorial ROC Graphs: Notes and Practical Considerations for ResearchersEmpirical and Theoretical Comparisons of Selected Criterion Functions for Document Clusteringhttps://puma.uni-kassel.de/bibtex/271ea6e1192ea34ac8193867c2512927a/hothohotho2007-12-20T20:55:26+01:00clustering comparisons criterion document evaluation index <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Ying Zhao" itemprop="url" href="/author/Ying%20Zhao"><span itemprop="name">Y. Zhao</span></a></span>, und <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="George Karypis" itemprop="url" href="/author/George%20Karypis"><span itemprop="name">G. Karypis</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>Machine Learning</em></span></span> <em><span itemtype="http://schema.org/PublicationVolume" itemscope="itemscope" itemprop="isPartOf"><span itemprop="volumeNumber">55 </span></span>(<span itemprop="issueNumber">3</span>):
<span itemprop="pagination">311-331</span></em> </span>(<em><span>2004<meta content="2004" itemprop="datePublished"/></span></em>)Thu Dec 20 20:55:26 CET 2007Machine Learning3311-331Empirical and Theoretical Comparisons of Selected Criterion Functions for Document Clustering552004clustering comparisons criterion document evaluation index dblpA geometric approach to cluster validity for normal mixtureshttps://puma.uni-kassel.de/bibtex/26c2a17868951a4a270f42332355d132b/hothohotho2007-12-20T20:42:44+01:00cluster evaluation index <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="J. C. Bezdek" itemprop="url" href="/author/J.%20C.%20Bezdek"><span itemprop="name">J. Bezdek</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="W. Q. Li" itemprop="url" href="/author/W.%20Q.%20Li"><span itemprop="name">W. Li</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Y. Attikiouzel" itemprop="url" href="/author/Y.%20Attikiouzel"><span itemprop="name">Y. Attikiouzel</span></a></span>, und <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="M. Windham" itemprop="url" href="/author/M.%20Windham"><span itemprop="name">M. Windham</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>Soft Computing - A Fusion of Foundations, Methodologies and Applications</em></span></span> <em><span itemtype="http://schema.org/PublicationVolume" itemscope="itemscope" itemprop="isPartOf"><span itemprop="volumeNumber">1 </span></span>(<span itemprop="issueNumber">4</span>):
<span itemprop="pagination">166--179</span></em> </span>(<em><span>Dezember 1997<meta content="Dezember 1997" itemprop="datePublished"/></span></em>)Thu Dec 20 20:42:44 CET 2007Soft Computing - A Fusion of Foundations, Methodologies and Applications#dec#4166--179A geometric approach to cluster validity for normal mixtures11997cluster evaluation index We study indices for choosing the correct number of components in a mixture of normal distributions. Previous studies have been confined to indices based wholly on probabilistic models. Viewing mixture decomposition as probabilistic clustering (where the emphasis is on partitioning for geometric substructure) as opposed to parametric estimation enables us to introduce both fuzzy and crisp measures of cluster validity for this problem. We presume the underlying samples to be unlabeled, and use the expectation-maximization (EM) algorithm to find clusters in the data. We test 16 probabilistic, 3 fuzzy and 4 crisp indices on 12 data sets that are samples from bivariate normal mixtures having either 3 or 6 components. Over three run averages based on different initializations of EM, 10 of the 23 indices tested for choosing the right number of mixture components were correct in at least 9 of the 12 trials. Among these were the fuzzy index of Xie-Beni, the crisp Davies-Bouldin index, and two crisp indices that are recent generalizations of Dunn's index.
ER -SpringerLink - Journal ArticleOn the use of spreading activation methods in automatic informationhttps://puma.uni-kassel.de/bibtex/2994aef0486e69095ee0d8ba5b3e3a91c/hothohotho2007-07-06T11:51:49+02:00*** activation evaluation ir msn network semantic spreading <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="G. Salton" itemprop="url" href="/author/G.%20Salton"><span itemprop="name">G. Salton</span></a></span>, und <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="C. Buckley" itemprop="url" href="/author/C.%20Buckley"><span itemprop="name">C. Buckley</span></a></span>. </span><span itemtype="http://schema.org/Book" itemscope="itemscope" itemprop="isPartOf"><em><span itemprop="name">SIGIR '88: Proceedings of the 11th annual international ACM SIGIR conference on Research and development in information retrieval</span>, </em></span><em>Seite <span itemprop="pagination">147--160</span>. </em><em>New York, NY, USA, </em><em><span itemprop="publisher">ACM Press</span>, </em>(<em><span>1988<meta content="1988" itemprop="datePublished"/></span></em>)Fri Jul 06 11:51:49 CEST 2007New York, NY, USASIGIR '88: Proceedings of the 11th annual international ACM SIGIR conference on Research and development in information retrieval147--160On the use of spreading activation methods in automatic information1988*** activation evaluation ir msn network semantic spreading Spreading activation methods have been recommended in information retrieval to expand the search vocabulary and to complement the retrieved document sets. The spreading activation strategy is reminiscent of earlier associative indexing and retrieval systems. Some spreading activation procedures are briefly described, and evaluation output is given, reflecting the effectiveness of one of the proposed procedures.On the use of spreading activation methods in automatic informationOn How to Perform a Gold Standard based Evaluation of Ontology Learninghttps://puma.uni-kassel.de/bibtex/20bfd502e363ef3f1523d77f972f08397/hothohotho2007-03-09T21:06:03+01:002006 evaluation learning ontology toread <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Klaas Dellschaft" itemprop="url" href="/author/Klaas%20Dellschaft"><span itemprop="name">K. Dellschaft</span></a></span>, und <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Steffen Staab" itemprop="url" href="/author/Steffen%20Staab"><span itemprop="name">S. Staab</span></a></span>. </span><span itemtype="http://schema.org/Book" itemscope="itemscope" itemprop="isPartOf"><em><span itemprop="name">In: Proc. of ISWC-2006 International Semantic Web Conference</span>, </em></span><em>Athens, GA, USA, </em><em><span itemprop="publisher">Springer, LNCS</span>, </em>(<em><span>November 2006<meta content="November 2006" itemprop="datePublished"/></span></em>)Fri Mar 09 21:06:03 CET 2007Athens, GA, USAIn: Proc. of ISWC-2006 International Semantic Web ConferenceNovemberOn How to Perform a Gold Standard based Evaluation of Ontology Learning20062006 evaluation learning ontology toread On How to Perform a Gold Standard based Evaluation of Ontology LearningComparing clusteringshttps://puma.uni-kassel.de/bibtex/24cfd500d784db1a78f58e6e42d34d31a/hothohotho2006-10-25T14:36:30+02:00clustering evaluation <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Marina Meila" itemprop="url" href="/author/Marina%20Meila"><span itemprop="name">M. Meila</span></a></span>. </span><span itemtype="http://schema.org/Book" itemscope="itemscope" itemprop="isPartOf"><em><span itemprop="name">Proc. of COLT 03</span>, </em></span>(<em><span>2003<meta content="2003" itemprop="datePublished"/></span></em>)Wed Oct 25 14:36:30 CEST 2006Proc. of COLT 03Comparing clusterings 2003clustering evaluation WordNet improves text document clusteringhttps://puma.uni-kassel.de/bibtex/2b03e58ecb17c09f8c09d1fd93fb24f90/hothohotho2006-06-27T09:41:28+02:00clustering text ontology wordnet evaluation myown SumSchool06 2003 <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="A. Hotho" itemprop="url" href="/author/A.%20Hotho"><span itemprop="name">A. Hotho</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="S. Staab" itemprop="url" href="/author/S.%20Staab"><span itemprop="name">S. Staab</span></a></span>, und <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><span itemtype="http://schema.org/Book" itemscope="itemscope" itemprop="isPartOf"><em><span itemprop="name">Proc. of the SIGIR 2003 Semantic Web Workshop</span>, </em></span><em>Toronto, Canada, </em>(<em><span>2003<meta content="2003" itemprop="datePublished"/></span></em>)Tue Jun 27 09:41:28 CEST 2006Toronto, CanadaProc. of the SIGIR 2003 Semantic Web WorkshopWordNet improves text document clustering2003clustering text ontology wordnet evaluation myown SumSchool06 2003 Evaluating Collaborative Filtering Recommender
Systemshttps://puma.uni-kassel.de/bibtex/2f688a96d523280b7e051648472fddd84/hothohotho2006-05-04T11:44:14+02:00recommender evaluation <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="J.L. Herlocker" itemprop="url" href="/author/J.L.%20Herlocker"><span itemprop="name">J. Herlocker</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="J.A. Konstan" itemprop="url" href="/author/J.A.%20Konstan"><span itemprop="name">J. Konstan</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="L.G. Terveen" itemprop="url" href="/author/L.G.%20Terveen"><span itemprop="name">L. Terveen</span></a></span>, und <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="J.T. Riedl" itemprop="url" href="/author/J.T.%20Riedl"><span itemprop="name">J. Riedl</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 Transactions on Information Systems</em></span></span> <em><span itemtype="http://schema.org/PublicationVolume" itemscope="itemscope" itemprop="isPartOf"><span itemprop="volumeNumber">22 </span></span>(<span itemprop="issueNumber">1</span>):
<span itemprop="pagination">5--53</span></em> </span>(<em><span>Januar 2004<meta content="Januar 2004" itemprop="datePublished"/></span></em>)Thu May 04 11:44:14 CEST 2006ACM Transactions on Information SystemsJanuary15--53{Evaluating Collaborative Filtering Recommender
Systems}222004recommender evaluation Clusteranalyse mit gemischt-skalierten Merkmalen: Abstrahierung vom Skalenniveauhttps://puma.uni-kassel.de/bibtex/2b5242e619ea59d5adc362d28453c478a/hothohotho2006-03-23T12:22:43+01:00clustering evaluation <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="N. Fickel" itemprop="url" href="/author/N.%20Fickel"><span itemprop="name">N. Fickel</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>Allgemeines Statistisches Archiv, Vandenhoeck & Ruprecht in Göttingen</em></span></span> <em><span itemtype="http://schema.org/PublicationVolume" itemscope="itemscope" itemprop="isPartOf"><span itemprop="volumeNumber">81 </span></span>(<span itemprop="issueNumber">3</span>):
<span itemprop="pagination">249-265</span></em> </span>(<em><span>1997<meta content="1997" itemprop="datePublished"/></span></em>)Thu Mar 23 12:22:43 CET 2006Allgemeines Statistisches Archiv, Vandenhoeck \& Ruprecht in Göttingen3249-265Clusteranalyse mit gemischt-skalierten Merkmalen: Abstrahierung vom Skalenniveau811997clustering evaluation Cluster Ensembles -- A Knowledge Reuse Framework for Combining Multiple Partitionshttps://puma.uni-kassel.de/bibtex/27fc2fdc5892130af320ac51b952149bf/hothohotho2006-03-23T11:54:07+01:00clustering evaluation combination ensembles <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Alexander Strehl" itemprop="url" href="/author/Alexander%20Strehl"><span itemprop="name">A. Strehl</span></a></span>, und <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Joydeep Ghosh" itemprop="url" href="/author/Joydeep%20Ghosh"><span itemprop="name">J. Ghosh</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 on Machine Learning Research (JMLR)</em></span></span> </span>(<em><span>Dezember 2002<meta content="Dezember 2002" itemprop="datePublished"/></span></em>)Thu Mar 23 11:54:07 CET 2006Journal on Machine Learning Research (JMLR)December583--617Cluster Ensembles -- A Knowledge Reuse Framework for Combining Multiple Partitions32002clustering evaluation combination ensembles This paper introduces the problem of combining multiple partitionings of a set of objects into a single consolidated clustering without accessing the features or algorithms that determined these partitionings. We first identify several application scenarios for the resultant 'knowledge reuse' framework that we call cluster ensembles. The cluster ensemble problem is then formalized as a combinatorial optimization problem in terms of shared mutual information. In addition to a direct maximization approach, we propose three effective and efficient techniques for obtaining high-quality combiners (consensus functions). The first combiner induces a similarity measure from the partitionings and then reclusters the objects. The second combiner is based on hypergraph partitioning. The third one collapses groups of clusters into meta-clusters which then compete for each object to determine the combined clustering. Due to the low computational costs of our techniques, it is quite feasible to use a supra-consensus function that evaluates all three approaches against the objective function and picks the best solution for a given situation. We evaluate the effectiveness of cluster ensembles in three qualitatively different application scenarios: (i) where the original clusters were formed based on non-identical sets of features, (ii) where the original clustering algorithms worked on non-identical sets of objects, and (iii) where a common data-set is used and the main purpose of combining multiple clusterings is to improve the quality and robustness of the solution. Promising results are obtained in all three situations for synthetic as well as real data-sets.Fuzzy Cluster Analysishttps://puma.uni-kassel.de/bibtex/28e77172459a6abf4c50dc14a5e1e0467/hothohotho2006-03-23T11:29:30+01:00clustering evaluation fuzzy overview <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Frank Höppner" itemprop="url" href="/author/Frank%20H%c3%b6ppner"><span itemprop="name">F. Höppner</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Frank Klawonn" itemprop="url" href="/author/Frank%20Klawonn"><span itemprop="name">F. Klawonn</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Rudolf Kruse" itemprop="url" href="/author/Rudolf%20Kruse"><span itemprop="name">R. Kruse</span></a></span>, und <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Thomas Runkler" itemprop="url" href="/author/Thomas%20Runkler"><span itemprop="name">T. Runkler</span></a></span>. </span><em><span itemprop="publisher">John Wiley & Sons, Inc.</span>, </em>(<em><span>1999<meta content="1999" itemprop="datePublished"/></span></em>)Thu Mar 23 11:29:30 CET 2006Fuzzy Cluster Analysis1999clustering evaluation fuzzy overview Nonparametric statistics for the behavioral scienceshttps://puma.uni-kassel.de/bibtex/2e2ac0dc902159ea0b174e65a72cb40e0/hothohotho2006-03-23T11:27:50+01:00evaluation statistic test <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="S. Siegel" itemprop="url" href="/author/S.%20Siegel"><span itemprop="name">S. Siegel</span></a></span>, und <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="N.J. Castellan" itemprop="url" href="/author/N.J.%20Castellan"><span itemprop="name">N. Castellan</span></a></span>. </span><em><span itemprop="publisher">McGraw--Hill, Inc.</span>, </em><em><span itemprop="bookEdition">Second</span> Edition, </em>(<em><span>1988<meta content="1988" itemprop="datePublished"/></span></em>)Thu Mar 23 11:27:50 CET 2006SecondNonparametric statistics for the behavioral sciences1988evaluation statistic test An examination of indexes for determining the number of clusters in binary data setshttps://puma.uni-kassel.de/bibtex/28d0a369818293ea71ff632882b988b01/hothohotho2006-03-23T11:26:20+01:00clustering evaluation <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="A. Weingessel" itemprop="url" href="/author/A.%20Weingessel"><span itemprop="name">A. Weingessel</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="E. Dimitriadou" itemprop="url" href="/author/E.%20Dimitriadou"><span itemprop="name">E. Dimitriadou</span></a></span>, und <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="S. Dolnicar" itemprop="url" href="/author/S.%20Dolnicar"><span itemprop="name">S. Dolnicar</span></a></span>. </span><em>Working Paper 29. </em><em><span itemprop="producer">SFB ``Adaptive Information Systems and Modeling in Economics and Management Science''</span>, </em>(<em><span>1999<meta content="1999" itemprop="datePublished"/></span></em>)Thu Mar 23 11:26:20 CET 2006Working Paper 29An examination of indexes for determining the number of clusters in binary data sets1999clustering evaluation