BibSonomy publications for /user/grahl/clusteringWed Jul 25 15:39:30 CEST 2007Data Mining and Knowledge Discovery4325-344Principal Direction Divisive Partitioning21998clustering 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 searches2003clustering text 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.Tue Jul 10 13:15:23 CEST 2007Research and Development in Information Retrieval206-213Deriving Concept Hierarchies from Text1999text conceptual clustering 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 Fri Nov 17 08:46:17 CET 2006Budva, MontenegroProceedings of the 3rd European Semantic Web ConferenceJuneContent Aggregation on Knowledge Bases using Graph Clustering2006ontology content graphtheory clustering aggregation Fri Nov 17 08:46:11 CET 2006WörlitzProc. 17. GI-Workshop ``Grundlagen von Datenbanken''MayTowards Content Aggregation on Knowledge Bases through Graph Clustering2005base graph knowledge clustering Fri Nov 17 08:46:05 CET 2006Journal of Web SemanticsFebruarySuper-Peer-Based Routing Strategies for {RDF}-Based Peer-to-Peer NetworksSpecial issue WWW 20032004clustering p2p routing 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 Networks2003routing clustering p2p 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 clustering2003clustering text data-mining ontology kdd Fri Nov 17 08:45:59 CET 2006Text Clustering Based on Background KnowledgeTechnical Report 4252003semantic ontology knowledge concept background analysis fca web formal text clustering 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.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 Structures28382003formal text semantic ontology fca clustering concept analysis 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.Fri Nov 17 08:45:54 CET 2006Proc. Fachgruppentreffen Maschinelles Lernen (FGML 2002)37-45Conceptual Clustering of Text Clusters2002concept formal text conceptual analysis clustering fca 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 Lattices2001clustering knowledge concept discovery lattices analysis formal iceberg kdd closed itemset fca conceptual Mon Oct 30 08:38:23 CET 2006DecCollaborative tagging as a tripartite network2005data-mining collaborative-filtering recommender-systems clustering 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.Mon Oct 30 08:38:23 CET 2006Collaborative Web Tagging Workshop at WWW2006, Edinburgh, ScotlandAutomated Tag Clustering: Improving search and exploration in the tag space2006tagging clustering Fri Oct 27 16:55:13 CEST 2006Proc. of COLT 03Comparing clusterings 2003clustering evaluation Fri Oct 27 16:55:13 CEST 2006Journal of the American Statistical Association 336846-850Objective criteria for the evaluation of clustering methods661971criticism clustering evaluation Fri Oct 27 16:55:13 CEST 2006Finding Groups in Data: An Introduction to Cluster Analysis1990clustering evaluation Fri Oct 27 16:55:13 CEST 2006Working Paper 29An examination of indexes for determining the number of clusters in binary data sets1999clustering evaluation Fri Oct 27 16:55:13 CEST 2006Fuzzy Cluster Analysis1999fuzzy evaluation clustering overview