BibliographyType,ISBN,Identifier,Author,Title,Journal,Volume,Number,Month,Pages,Year,Address,Note,URL,Booktitle,Chapter,Edition,Series,Editor,Publisher,ReportType,Howpublished,Institution,Organizations,School,Annote,Custom1,Custom2,Custom3,Custom4,Custom5
7,"","keyhere","Carpineto, Claudio & Romano, Giovanni","A lattice conceptual clustering system and its application to browsing retrieval","Machine Learning",24,2,"August","95--122",1996,"","","http://dx.doi.org/10.1007/BF00058654","","","","","","","","","","","","","The theory of concept (or Galois) lattices provides a simple and formal approach to conceptual clustering. In this paper we present GALOIS, a system that automates and applies this theory. The algorithm utilized by GALOIS to build a concept lattice is incremental and efficient, each update being done in time at most quadratic in the number of objects in the lattice. Also, the algorithm may incorporate background information into the lattice, and through clustering, extend the scope of the theory. The application we present is concerned with information retrieval via browsing, for which we argue that concept lattices may represent major support structures. We describe a prototype user interface for browsing through the concept lattice of a document-term relation, possibly enriched with a thesaurus of terms. An experimental evaluation of the system performed on a medium-sized bibliographic database shows good retrieval performance and a significant improvement after the introduction of background knowledge. ER -","","analysis carpineto clustering concept fca formal information ir retrieval ","",""
6,"","FalBarSpi07","Falkowski, Tanja; Barth, Anja & Spiliopoulou, Myra","DENGRAPH: A Density-based Community Detection Algorithm","",,,"","112-115",2007,"","","http://wwwiti.cs.uni-magdeburg.de/~tfalkows/publ/2007/WI_FalBarSpi07.pdf","In Proc. of the 2007 IEEE / WIC / ACM International Conference on Web Intelligence,","","","","","","","","","","","","","","algorithm based clustering community density detection ","",""
10,"","golder05structure","Golder, Scott & Huberman, Bernardo A.","The Structure of Collaborative Tagging Systems","",,,"Aug","",2005,"","","http://arxiv.org/abs/cs.DL/0508082","","","","","","","","","","","","","","","clustering collaborative_tagging delicious emergence folksonomies folksonomy information_organization self-organization seminar2006 social-networks socialtagging tag tagging tags ","",""
6,"978-3-86010-907-6","grahl07conceptualKdml","Grahl, Miranda; Hotho, Andreas & Stumme, Gerd","Conceptual Clustering of Social Bookmark Sites","",,,"sep","50-54",2007,"","","http://www.kde.cs.uni-kassel.de/hotho/pub/2007/kdml_recommender_final.pdf","Workshop Proceedings of Lernen - Wissensentdeckung - Adaptivität (LWA 2007)","","","","Hinneburg, Alexander","Martin-Luther-Universität Halle-Wittenberg","","","","","","","","","2007 Social bookmark bookmarking clustering collaborative conceptual folksonomies folksonomy myown social tagging tagorapub ","",""
6,"","hotho03explaining","Hotho, Andreas; Staab, Steffen & Stumme, Gerd","Explaining Text Clustering Results using Semantic Structures","",2838,,"","217-228",2003,"Heidelberg","","http://www.kde.cs.uni-kassel.de/stumme/papers/2003/hotho2003explaining.pdf","Knowledge Discovery in Databases: PKDD 2003, 7th European Conference on Principles and Practice of Knowledge Discovery in Databases","","","LNAI","Lavra\v{c}, Nada; Gamberger, Dragan & Todorovski, Hendrik BlockeelLjupco","Springer","","","","","","","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.","alpha","2003 analysis clustering concept fca formal myown ontologies semantic semantics text ","",""
6,"","hotho03wordnet","Hotho, A; Staab, S. & Stumme, G.","Wordnet improves text document clustering","",,,"","",2003,"Toronto","","http://www.kde.cs.uni-kassel.de/stumme/papers/2003/hotho2003wordnet.pdf","Proc. SIGIR Semantic Web Workshop","","","","","","","","","","","","","alpha","2003 clustering data discovery document information ir kdd kmeans knowledge mining myown retrieval text wordnet ","",""
6,"","hotho02conceptualclustering","Hotho, A. & Stumme, G.","Conceptual Clustering of Text Clusters","",,,"","37-45",2002,"","","http://www.kde.cs.uni-kassel.de/stumme/papers/2002/FGML02.pdf","Proc. Fachgruppentreffen Maschinelles Lernen (FGML 2002)","","","","K\'okai, G. & Zeidler, J.","","","","","","","","","alpha","2002 analysis clustering concept conceptual fca formal myown text ","",""
10,"","noack08modularity","Noack, Andreas","Modularity clustering is force-directed layout","",,,"","",2008,"","","http://www.citebase.org/abstract?id=oai:arXiv.org:0807.4052","","","","","","","","","","","",""," Two natural and widely used representations for the community structure of networks are clusterings, which partition the vertex set into disjoint subsets, and layouts, which assign the vertices to positions in a metric space. This paper unifies prominent characterizations of layout quality and clustering quality, by showing that energy models of pairwise attraction and repulsion subsume Newman and Girvan's modularity measure. Layouts with optimal energy are relaxations of, and are thus consistent with, clusterings with optimal modularity, which is of practical relevance because both representations are complementary and often used together.","","clustering communities community graph layout modularity network sna ","",""
6,"","schmitz2006content","Schmitz, Christoph; Hotho, Andreas; Jäschke, Robert & Stumme, Gerd","Content Aggregation on Knowledge Bases using Graph Clustering","",4011,,"","530-544",2006,"Heidelberg","","http://www.kde.cs.uni-kassel.de/stumme/papers/2006/schmitz2006content.pdf","The Semantic Web: Research and Applications","","","LNAI","Sure, York & Domingue, John","Springer","","","","","","","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.","","2006 aggregation clustering content graph l3s myown nepomuk ontologies ontology seminar2006 theory ","",""
6,"","stumme01conceptualclustering","Stumme, G.; Taouil, R.; Bastide, Y. & Lakhal, L.","Conceptual Clustering with Iceberg Concept Lattices","",,,"October","",2001,"Universität Dortmund 763","{P}art of \cite{stumme02computing}","http://www.kde.cs.uni-kassel.de/stumme/papers/2001/FGML01.pdf","Proc. GI-Fachgruppentreffen Maschinelles Lernen (FGML'01)","","","","Klinkenberg, R.; Rüping, S.; Fick, A.; Henze, N.; Herzog, C.; Molitor, R. & Schröder, O.","","","","","","","","","alpha","2001 analysis closed clustering concept conceptual discovery fca formal iceberg itemsets kdd knowledge lattices myown ","",""