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 bioterrorism ER - TY - THES AU - Bade, Korinna T1 - Personalized Hierarchical Structuring PY - 2009/ PB - Otto-von-Guericke-Universitat Magdeburg SP - EP - UR - DO - KW - clustering KW - hierarchical L1 - N1 - N1 - AB - ER - TY - JOUR AU - Carpineto, Claudio AU - Osiński, Stanislaw AU - Romano, Giovanni AU - Weiss, Dawid T1 - A survey of Web clustering engines JO - ACM Comput. Surv. PY - 2009/07 VL - 41 IS - SP - 17:1 EP - 17:38 UR - http://doi.acm.org/10.1145/1541880.1541884 DO - 10.1145/1541880.1541884 KW - clustering KW - web KW - bachelor:2011:bachmann L1 - SN - N1 - N1 - AB - 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. ER - TY - CONF AU - Lu, Caimei AU - Chen, Xin AU - Park, E. K. A2 - T1 - Exploit the tripartite network of social tagging for web clustering T2 - Proceeding of the 18th ACM conference on Information and knowledge management PB - ACM C1 - New York, NY, USA PY - 2009/ CY - VL - IS - SP - 1545 EP - 1548 UR - http://doi.acm.org/10.1145/1645953.1646167 DO - 10.1145/1645953.1646167 KW - clustering KW - web KW - bachelor:2011:bachmann L1 - SN - 978-1-60558-512-3 N1 - N1 - AB - 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. ER - TY - GEN AU - Nedjah, Nadia A2 - T1 - Intelligent text categorization and clustering JO - PB - Springer C1 - Berlin PY - 2009/ VL - IS - SP - EP - UR - http://rave.ohiolink.edu/ebooks/ebc/9783540856443 DO - KW - clustering L1 - N1 - Intelligent Text Categorization and Clustering N1 - AB - "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. ER - TY - JOUR AU - Leicht, E. A. AU - Newman, M. E. J. T1 - Community Structure in Directed Networks JO - Phys. Rev. Lett. PY - 2008/03 VL - 100 IS - 11 SP - EP - UR - DO - 10.1103/PhysRevLett.100.118703 KW - 2011 KW - clustering KW - community KW - directed KW - modularity KW - networks L1 - SN - N1 - N1 - AB - ER - TY - CHAP AU - Brandes, Ulrik AU - Delling, Daniel AU - Gaertler, Marco AU - Görke, Robert AU - Hoefer, Martin AU - Nikoloski, Zoran AU - Wagner, Dorothea A2 - Brandstädt, Andreas A2 - Kratsch, Dieter A2 - Müller, Haiko T1 - On Finding Graph Clusterings with Maximum Modularity T2 - Graph-Theoretic Concepts in Computer Science PB - Springer C1 - Berlin / Heidelberg PY - 2007/ VL - 4769 IS - SP - 121 EP - 132 UR - http://dx.doi.org/10.1007/978-3-540-74839-7_12 DO - 10.1007/978-3-540-74839-7_12 KW - clustering KW - graph KW - modularity KW - theory L1 - SN - 978-3-540-74838-0 N1 - Abstract - SpringerLink N1 - AB - 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. ER - TY - CONF AU - Grahl, Miranda AU - Hotho, Andreas AU - Stumme, Gerd A2 - Hinneburg, Alexander T1 - Conceptual Clustering of Social Bookmark Sites T2 - Workshop Proceedings of Lernen -- Wissensentdeckung -- Adaptivität (LWA 2007) PB - Martin-Luther-Universität Halle-Wittenberg C1 - PY - 2007/10 CY - VL - IS - SP - 50 EP - 54 UR - http://www.kde.cs.uni-kassel.de/hotho/pub/2007/kdml_recommender_final.pdf DO - KW - 2007 KW - Social KW - bookmark KW - bookmarking KW - clustering KW - collaborative KW - conceptual KW - folksonomies KW - folksonomy KW - itegpub KW - myown KW - social KW - tagging KW - tagorapub L1 - SN - 978-3-86010-907-6 N1 - N1 - AB - ER - TY - JOUR AU - Newman, M. E. J. T1 - Modularity and community structure in networks JO - Proceedings of the National Academy of Sciences PY - 2006/ VL - 103 IS - 23 SP - 8577 EP - 8582 UR - DO - 10.1073/pnas.0601602103 KW - clustering KW - community KW - graph KW - modularity KW - network KW - structure L1 - SN - N1 - N1 - AB - 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. ER - TY - CONF AU - Schmitz, Christoph AU - Hotho, Andreas AU - Jäschke, Robert AU - Stumme, Gerd A2 - Sure, York A2 - Domingue, John T1 - Content Aggregation on Knowledge Bases using Graph Clustering T2 - The Semantic Web: Research and Applications PB - Springer C1 - Heidelberg PY - 2006/ CY - VL - 4011 IS - SP - 530 EP - 544 UR - http://www.kde.cs.uni-kassel.de/stumme/papers/2006/schmitz2006content.pdf DO - KW - 2006 KW - aggregation KW - clustering KW - content KW - graph KW - itegpub KW - l3s KW - myown KW - nepomuk KW - ontologies KW - ontology KW - seminar2006 KW - theory L1 - SN - N1 - N1 - AB - 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. ER - TY - JOUR AU - Dhillon, Inderjit S. AU - Modha, Dharmendra S. AU - Spangler, W. Scott T1 - Class visualization of high-dimensional data with applications JO - Computational Statistics & Data Analysis PY - 2002/november VL - 41 IS - 1 SP - 59 EP - 90 UR - http://www.cs.utexas.edu/~inderjit/public_papers/csda.pdf DO - KW - alphaworks KW - cluster KW - clustering KW - dm KW - visualization L1 - SN - N1 - N1 - AB - No abstract is available for this item. ER - TY - CONF AU - Stumme, G. AU - Taouil, R. AU - Bastide, Y. AU - Lakhal, L. A2 - Klinkenberg, R. A2 - Rüping, S. A2 - Fick, A. A2 - Henze, N. A2 - Herzog, C. A2 - Molitor, R. A2 - Schröder, O. T1 - Conceptual Clustering with Iceberg Concept Lattices T2 - Proc. GI-Fachgruppentreffen Maschinelles Lernen (FGML'01) PB - C1 - Universität Dortmund 763 PY - 2001/october CY - VL - IS - SP - EP - UR - http://www.kde.cs.uni-kassel.de/stumme/papers/2001/FGML01.pdf DO - KW - 2001 KW - analysis KW - closed KW - clustering KW - concept KW - conceptual KW - discovery KW - fca KW - formal KW - iceberg KW - itemsets KW - kdd KW - knowledge KW - lattices L1 - SN - N1 - Publications of Gerd Stumme N1 - AB - ER - TY - CONF AU - Toivonen, Jarmo AU - Visa, Ari AU - Vesanen, Tomi AU - Back, Barbro AU - Vanharanta, Hannu A2 - Perner, Petra T1 - Validation of Text Clustering Based on Document Contents. T2 - MLDM PB - Springer C1 - PY - 2001/ CY - VL - 2123 IS - SP - 184 EP - 195 UR - http://dblp.uni-trier.de/db/conf/mldm/mldm2001.html#ToivonenVVBV01 DO - KW - clustering KW - text KW - toread L1 - SN - 3-540-42359-1 N1 - N1 - AB - ER - TY - JOUR AU - Jain, A. K. AU - Murty, M. N. AU - Flynn, P. J. T1 - Data Clustering: A Review JO - ACM Comput. Surv. PY - 1999/10 VL - 31 IS - 3 SP - 264 EP - 323 UR - http://doi.acm.org/10.1145/331499.331504 DO - 10.1145/331499.331504 KW - clustering KW - overview KW - review L1 - SN - N1 - Data clustering N1 - AB - 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. ER - TY - JOUR AU - Jain, A. K. AU - Murty, M. N. AU - Flynn, P. J. T1 - Data clustering: a review JO - ACM Comput. Surv. PY - 1999/10 VL - 31 IS - 3 SP - 264 EP - 323 UR - http://doi.acm.org/10.1145/331499.331504 DO - 10.1145/331499.331504 KW - clustering L1 - SN - N1 - Data clustering N1 - AB - ER - TY - CONF AU - Zhang, Tian AU - Ramakrishnan, Raghu AU - Livny, Miron A2 - T1 - BIRCH: An Efficient Data Clustering Method for Very Large Databases T2 - Proceedings of the 1996 ACM SIGMOD International Conference on Management of Data PB - ACM C1 - New York, NY, USA PY - 1996/ CY - VL - IS - SP - 103 EP - 114 UR - http://doi.acm.org/10.1145/233269.233324 DO - 10.1145/233269.233324 KW - birch KW - clustering KW - kdd L1 - SN - 0-89791-794-4 N1 - BIRCH N1 - AB - ER -