http://www.bibsonomy.org/burst/user/grahl/clusteringBibSonomy BuRST Feed for /user/grahl/clustering2008-09-06T20:12:21+02:00Principal Direction Divisive Partitioning - Boley (ResearchIndex)http://www.bibsonomy.org/bibtex/2b3c83d7f8c26c7ea645092eac767abca/grahlgrahl2007-07-25T15:39:30+02:00clustering <span style="color:#555555;">Daniel <a href="http://www.bibsonomy.org/author/Boley">Boley</a> </span><em>Data Mining and Knowledge Discovery</em><em>2(4):325-344</em>(<em>1998</em>)Wed Jul 25 15:39:30 CEST 2007Data Mining and Knowledge Discovery4325-344Principal Direction Divisive Partitioning21998clustering : SIGIR '03, Generating hierarchical summaries for ...http://www.bibsonomy.org/bibtex/2519d345504436bab425c0c8ad5d89a91/grahlgrahl2007-07-10T13:16:52+02:00text clustering croft <span style="color:#555555;">Dawn J. <a href="http://www.bibsonomy.org/author/Lawrie">Lawrie</a> und W. <a href="http://www.bibsonomy.org/author/Bruce Croft">Bruce Croft</a> </span><em>SIGIR '03: Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval, </em><em>Seite457--458. </em><em>New York, NY, USA, </em><em>ACM Press, </em>(<em>2003</em>)Tue Jul 10 13:16:52 CEST 2007New York, NY, USASIGIR '03: Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval457--458Generating hierarchical summaries for web searches2003text clustering croft Hierarchies provide a means of organizing, summarizing and accessing information. We describe a method for automatically generating hierarchies from small collections of text, and then apply this technique to summarizing the documents retrieved by a search engine.Deriving Concept Hierarchies From Text - Sanderson, Croft (ResearchIndex)http://www.bibsonomy.org/bibtex/2c562ccc8d54fb6c3f32dcbe722cef386/grahlgrahl2007-07-10T13:15:23+02:00text conceptual clustering <span style="color:#555555;">Mark <a href="http://www.bibsonomy.org/author/Sanderson">Sanderson</a> und W. <a href="http://www.bibsonomy.org/author/Bruce Croft">Bruce Croft</a> </span><em>Research and Development in Information Retrieval, </em><em>Seite206-213. </em>(<em>1999</em>)Tue Jul 10 13:15:23 CEST 2007Research and Development in Information Retrieval206-213Deriving Concept Hierarchies from Text1999text conceptual clustering http://www.bibsonomy.org/bibtex/2346d1db87c3bda5fcf4ec5f92a75e16a/grahlgrahl2007-03-30T14:51:03+02:00clustering <span style="color:#555555;">Andrew <a href="http://www.bibsonomy.org/author/McCallum">McCallum</a> und Kamal <a href="http://www.bibsonomy.org/author/Nigam">Nigam</a> und Lyle H. <a href="http://www.bibsonomy.org/author/Ungar">Ungar</a> </span><em>KDD '00: Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining, </em><em>Seite169--178. </em><em>New York, NY, USA, </em><em>ACM Press, </em>(<em>2000</em>)Fri Mar 30 14:51:03 CEST 2007New York, NY, USAKDD '00: Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining169--178Efficient clustering of high-dimensional data sets with application to reference matching2000clustering http://www.bibsonomy.org/bibtex/27d738e62dffd04f709e66de94c6dee89/grahlgrahl2006-11-17T08:46:17+01:00aggregation content graphtheory clustering ontology <span style="color:#555555;">Christoph <a href="http://www.bibsonomy.org/author/Schmitz">Schmitz</a> und Andreas <a href="http://www.bibsonomy.org/author/Hotho">Hotho</a> und Robert <a href="http://www.bibsonomy.org/author/Jäschke">J&#228;schke</a> und Gerd <a href="http://www.bibsonomy.org/author/Stumme">Stumme</a> </span><em>Proceedings of the 3rd European Semantic Web Conference, </em><em>Budva, Montenegro, </em><em>June2006. </em>Fri Nov 17 08:46:17 CET 2006Budva, MontenegroProceedings of the 3rd European Semantic Web ConferenceJuneContent Aggregation on Knowledge Bases using Graph Clustering2006aggregation content graphtheory clustering ontology http://www.bibsonomy.org/bibtex/2d4de2a97be96bef44e907de9ddf7719e/grahlgrahl2006-11-17T08:46:11+01:00clustering graph knowledge base <span style="color:#555555;">Christoph <a href="http://www.bibsonomy.org/author/Schmitz">Schmitz</a> </span><em>Proc. 17. GI-Workshop ``Grundlagen von Datenbanken'', </em><em>W&#246;rlitz, </em><em>May2005. </em>Fri Nov 17 08:46:11 CET 2006WörlitzProc. 17. GI-Workshop ``Grundlagen von Datenbanken''MayTowards Content Aggregation on Knowledge Bases through Graph Clustering2005clustering graph knowledge base http://www.bibsonomy.org/bibtex/25a5f7698713bb9af488805e9b88c4922/grahlgrahl2006-11-17T08:46:05+01:00routing p2p clustering <span style="color:#555555;">Wolfgang <a href="http://www.bibsonomy.org/author/Nejdl">Nejdl</a> und Martin <a href="http://www.bibsonomy.org/author/Wolpers">Wolpers</a> und Wolf <a href="http://www.bibsonomy.org/author/Siberski">Siberski</a> und Christoph <a href="http://www.bibsonomy.org/author/Schmitz">Schmitz</a> und Mario <a href="http://www.bibsonomy.org/author/Schlosser">Schlosser</a> und Ingo <a href="http://www.bibsonomy.org/author/Brunkhorst">Brunkhorst</a> und Alexander <a href="http://www.bibsonomy.org/author/Löser">L&#246;ser</a> </span><em>Journal of Web Semantics</em><em>February2004. </em>Fri Nov 17 08:46:05 CET 2006Journal of Web SemanticsFebruarySuper-Peer-Based Routing Strategies for {RDF}-Based Peer-to-Peer NetworksSpecial issue WWW 20032004routing p2p clustering http://www.bibsonomy.org/bibtex/2710a9b392f9cd7020a886b375e44c678/grahlgrahl2006-11-17T08:45:59+01:00p2p routing clustering <span style="color:#555555;">Wolfgang <a href="http://www.bibsonomy.org/author/Nejdl">Nejdl</a> und Martin <a href="http://www.bibsonomy.org/author/Wolpers">Wolpers</a> und Wolf <a href="http://www.bibsonomy.org/author/Siberski">Siberski</a> und Christoph <a href="http://www.bibsonomy.org/author/Schmitz">Schmitz</a> und Mario <a href="http://www.bibsonomy.org/author/Schlosser">Schlosser</a> und Ingo <a href="http://www.bibsonomy.org/author/Brunkhorst">Brunkhorst</a> und Alexander <a href="http://www.bibsonomy.org/author/Löser">L&#246;ser</a> </span><em>Proceedings of the 12th International World Wide Web Conference, </em><em>Budapest, </em><em>May2003. </em>Fri Nov 17 08:45:59 CET 2006BudapestProceedings of the 12th International World Wide Web ConferenceMaySuper-Peer-Based Routing and Clustering Strategies for {RDF}-Based Peer-To-Peer Networks2003p2p routing clustering http://www.bibsonomy.org/bibtex/257a39c81cff1982dbefed529be934bee/grahlgrahl2006-11-17T08:45:59+01:00kdd clustering ontology data-mining text <span style="color:#555555;">Andreas <a href="http://www.bibsonomy.org/author/Hotho">Hotho</a> und Steffen <a href="http://www.bibsonomy.org/author/Staab">Staab</a> und Gerd <a href="http://www.bibsonomy.org/author/Stumme">Stumme</a> </span><em>Proceedings of the 2003 IEEE International Conference on Data Mining, </em><em>Seite541-544 (Poster. </em><em>Melbourne, Florida, </em><em>IEEE Computer Society, </em><em>November 19-22,2003. </em>Fri Nov 17 08:45:59 CET 2006Melbourne, FloridaProceedings of the 2003 IEEE International Conference on Data MiningNovember 19-22,541-544 (PosterOntologies improve text document clustering2003kdd clustering ontology data-mining text http://www.bibsonomy.org/bibtex/261d58db419af0dbc3681432588219c3d/grahlgrahl2006-11-17T08:45:59+01:00analysis knowledge fca formal background semantic concept web clustering text ontology <span style="color:#555555;">Andreas <a href="http://www.bibsonomy.org/author/Hotho">Hotho</a> und Steffen <a href="http://www.bibsonomy.org/author/Staab">Staab</a> und Gerd <a href="http://www.bibsonomy.org/author/Stumme">Stumme</a> </span><em>Technical Report, </em><em>Volume425. </em><em>University of Karlsruhe, Institute AIFB, </em>(<em>2003</em>)Fri Nov 17 08:45:59 CET 2006Text Clustering Based on Background KnowledgeTechnical Report 4252003analysis knowledge fca formal background semantic concept web clustering text ontology Text document clustering plays an important role in providing intuitive navigation and browsing mechanisms by organizing large amounts of information into a small number of meaningful clusters. Standard partitional or agglomerative clustering methods efficiently compute results to this end. However, the bag of words representation used for these clustering methods is often unsatisfactory as it ignores relationships between important terms that do not co-occur literally. Also, it is mostly left to the user to find out why a particular partitioning has been achieved, because it is only specified extensionally. In order to deal with the two problems, we integrate background knowledge into the process of clustering text documents. First, we preprocess the texts, enriching their representations by background knowledge provided in a core ontology — in our application Wordnet. Then, we cluster the documents by a partitional algorithm. Our experimental evaluation on Reuters newsfeeds compares clustering results with pre-categorizations of news. In the experiments, improvements of results by background knowledge compared to the baseline can be shown for many interesting tasks. Second, the clustering partitions the large number of documents to a relatively small number of clusters, which may then be analyzed by conceptual clustering. In our approach, we applied Formal Concept Analysis. Conceptual clustering techniques are known to be too slow for directly clustering several hundreds of documents, but they give an intensional account of cluster results. They allow for a concise description of commonalities and distinctions of different clusters. With background knowledge they even find abstractions like “food” (vs. specializations like “beef” or “corn”). Thus, in our approach, partitional clustering reduces first the size of the problem such that it becomes tractable for conceptual clustering, which then facilitates the understanding of the results.http://www.bibsonomy.org/bibtex/2031e878767fcacab5ba54500eea8e33c/grahlgrahl2006-11-17T08:45:59+01:00semantic formal analysis concept text clustering ontology fca <span style="color:#555555;">Andreas <a href="http://www.bibsonomy.org/author/Hotho">Hotho</a> und Steffen <a href="http://www.bibsonomy.org/author/Staab">Staab</a> und Gerd <a href="http://www.bibsonomy.org/author/Stumme">Stumme</a> </span><em>Knowledge Discovery in Databases: PKDD 2003, 7th European Conference on Principles and Practice of Knowledge Discovery in Databases, </em><em>Volume2838vonLNAI, </em><em>Seite217-228. </em><em>Heidelberg, </em><em>Springer, </em>(<em>2003</em>)Fri Nov 17 08:45:59 CET 2006HeidelbergKnowledge Discovery in Databases: PKDD 2003, 7th European Conference on Principles and Practice of Knowledge Discovery in Databases217-228LNAIExplaining Text Clustering Results using Semantic Structures28382003semantic formal analysis concept text clustering ontology fca Common text clustering techniques offer rather poor capabilities for explaining to their users why a particular result has been achieved. They have the disadvantage that they do not relate semantically nearby terms and that they cannot explain how resulting clusters are related to each other. In this paper, we discuss a way of integrating a large thesaurus and the computation of lattices of resulting clusters into common text clustering in order to overcome these two problems. As its major result, our approach achieves an explanation using an appropriate level of granularity at the concept level as well as an appropriate size and complexity of the explaining lattice of resulting clusters.http://www.bibsonomy.org/bibtex/2e253c44552a046fe90236274bcfeab13/grahlgrahl2006-11-17T08:45:54+01:00text concept clustering fca formal conceptual analysis <span style="color:#555555;">A. <a href="http://www.bibsonomy.org/author/Hotho">Hotho</a> und G. <a href="http://www.bibsonomy.org/author/Stumme">Stumme</a> </span><em>Proc. Fachgruppentreffen Maschinelles Lernen (FGML 2002), </em><em>Seite37-45. </em>(<em>2002</em>)Fri Nov 17 08:45:54 CET 2006Proc. Fachgruppentreffen Maschinelles Lernen (FGML 2002)37-45Conceptual Clustering of Text Clusters2002text concept clustering fca formal conceptual analysis http://www.bibsonomy.org/bibtex/2f4ec21d5f63dbc213a3a6eae076c4b62/grahlgrahl2006-11-17T08:45:47+01:00closed kdd itemset iceberg formal fca analysis concept discovery conceptual lattices knowledge clustering <span style="color:#555555;">G. <a href="http://www.bibsonomy.org/author/Stumme">Stumme</a> und R. <a href="http://www.bibsonomy.org/author/Taouil">Taouil</a> und Y. <a href="http://www.bibsonomy.org/author/Bastide">Bastide</a> und L. <a href="http://www.bibsonomy.org/author/Lakhal">Lakhal</a> </span><em>Proc. GI-Fachgruppentreffen Maschinelles Lernen (FGML'01), </em><em>Universit&#228;t Dortmund 763, </em><em>October2001. </em><em>Part of stumme02computing . </em>Fri Nov 17 08:45:47 CET 2006Universität Dortmund 763Proc. GI-Fachgruppentreffen Maschinelles Lernen (FGML'01)October{P}art of \cite{stumme02computing}Conceptual Clustering with Iceberg Concept Lattices2001closed kdd itemset iceberg formal fca analysis concept discovery conceptual lattices knowledge clustering bibliografica tesihttp://www.bibsonomy.org/bibtex/265c6f348a54f872fb3e60b4bd64b485b/grahlgrahl2006-10-30T08:38:23+01:00recommender-systems clustering collaborative-filtering data-mining tagging <span style="color:#555555;">R. <a href="http://www.bibsonomy.org/author/Lambiotte">Lambiotte</a> und M. <a href="http://www.bibsonomy.org/author/Ausloos">Ausloos</a> </span><em>Dec2005. </em>Mon Oct 30 08:38:23 CET 2006DecCollaborative tagging as a tripartite network2005recommender-systems clustering collaborative-filtering data-mining tagging We describe online collaborative communities by tripartite networks, the nodes being persons, items and tags. We introduce projection methods in order to uncover the structures of the networks, i.e. communities of users, genre families... <br />To do so, we focus on the correlations between the nodes, depending on their profiles, and use percolation techniques that consist in removing less correlated links and observing the shaping of disconnected islands. The structuring of the network is visualised by using a tree representation. The notion of diversity in the system is also discussed.bibliografica tesihttp://www.bibsonomy.org/bibtex/276b741061fab004645c3119db5a17bc3/grahlgrahl2006-10-30T08:38:23+01:00clustering tagging <span style="color:#555555;">Grigory <a href="http://www.bibsonomy.org/author/Begelman">Begelman</a> und Philipp <a href="http://www.bibsonomy.org/author/Keller">Keller</a> und Frank <a href="http://www.bibsonomy.org/author/Smadja">Smadja</a> </span><em>Collaborative Web Tagging Workshop at WWW2006, Edinburgh, Scotland, </em>(<em>2006</em>)Mon Oct 30 08:38:23 CET 2006Collaborative Web Tagging Workshop at WWW2006, Edinburgh, ScotlandAutomated Tag Clustering: Improving search and exploration in the tag space2006clustering tagging http://www.bibsonomy.org/bibtex/24cfd500d784db1a78f58e6e42d34d31a/grahlgrahl2006-10-27T16:55:13+02:00evaluation clustering <span style="color:#555555;">Marina <a href="http://www.bibsonomy.org/author/Meila">Meila</a> </span><em>Proc. of COLT 03, </em>(<em>2003</em>)Fri Oct 27 16:55:13 CEST 2006Proc. of COLT 03Comparing clusterings 2003evaluation clustering http://www.bibsonomy.org/bibtex/2fd52548cb4bcd8e83dd27e4b55eff1f3/grahlgrahl2006-10-27T16:55:13+02:00clustering evaluation criticism <span style="color:#555555;">W.M. <a href="http://www.bibsonomy.org/author/Rand">Rand</a> </span><em>Journal of the American Statistical Association</em><em>66(336):846-850</em>(<em>1971</em>)Fri Oct 27 16:55:13 CEST 2006Journal of the American Statistical Association 336846-850Objective criteria for the evaluation of clustering methods661971clustering evaluation criticism http://www.bibsonomy.org/bibtex/26f403cbc240f28b3aa461b19aee77238/grahlgrahl2006-10-27T16:55:13+02:00evaluation clustering <span style="color:#555555;">L. <a href="http://www.bibsonomy.org/author/Kaufman">Kaufman</a> und P. J. <a href="http://www.bibsonomy.org/author/Rousseeuw">Rousseeuw</a> </span><em>John Wiley, </em>(<em>1990</em>)Fri Oct 27 16:55:13 CEST 2006Finding Groups in Data: An Introduction to Cluster Analysis1990evaluation clustering http://www.bibsonomy.org/bibtex/28d0a369818293ea71ff632882b988b01/grahlgrahl2006-10-27T16:55:13+02:00evaluation clustering <span style="color:#555555;">A. <a href="http://www.bibsonomy.org/author/Weingessel">Weingessel</a> und E. <a href="http://www.bibsonomy.org/author/Dimitriadou">Dimitriadou</a> und S. <a href="http://www.bibsonomy.org/author/Dolnicar">Dolnicar</a> </span><em>Working Paper 29. </em><em>SFB ``Adaptive Information Systems and Modeling in Economics and Management Science'', </em>(<em>1999</em>)Fri Oct 27 16:55:13 CEST 2006Working Paper 29An examination of indexes for determining the number of clusters in binary data sets1999evaluation clustering http://www.bibsonomy.org/bibtex/28e77172459a6abf4c50dc14a5e1e0467/grahlgrahl2006-10-27T16:55:13+02:00fuzzy clustering evaluation overview <span style="color:#555555;">Frank <a href="http://www.bibsonomy.org/author/Höppner">H&#246;ppner</a> und Frank <a href="http://www.bibsonomy.org/author/Klawonn">Klawonn</a> und Rudolf <a href="http://www.bibsonomy.org/author/Kruse">Kruse</a> und Thomas <a href="http://www.bibsonomy.org/author/Runkler">Runkler</a> </span><em>John Wiley &amp; Sons, Inc., </em>(<em>1999</em>)Fri Oct 27 16:55:13 CEST 2006Fuzzy Cluster Analysis1999fuzzy clustering evaluation overview