Aggarwal, C. C. & Reddy, C. K. (Hrsg.): Data Clustering: Algorithms and Applications. CRC Press, 2014
Radelaar, J.; Boor, A.-J.; Vandic, D.; van Dam, J.-W.; Hogenboom, F. & Frasincar, F.: Improving the Exploration of Tag Spaces Using Automated Tag Clustering. In: Auer, S.; Díaz, O. & Papadopoulos, G. (Hrsg.): Web Engineering. Springer Berlin / Heidelberg, 2011 (Lecture Notes in Computer Science 6757), S. 274-288
Ren, F.-X.; Cheng, X.-Q. & Shen, H.-W.: Modeling the clustering in citation networks. , 2011
It has been known for a long time that citation networks are always highly clustered, such as the existences of abundant triangles and high clustering coefficient. In a growth model, one typical way to produce clustering is using the trid formation mechanism. However, we find that this mechanism fails to generate enough triangles in a real-world citation network. By analyzing the network, it is found that one paper always cites papers that are already highly connected. We point out that the highly connected papers may refer to similar research topic and one subsequent paper tends to cite all of them. Based on this assumption, we propose a growth model for citation networks in which a new paper i firstly attaches to one relevant paper j and then with a probability links those papers in the same clique to which j belongs. We compare our model to two real-world citation networks - one on a special research area and the other on multidisciplinary sciences. Results show that for the two networks the in-degree distributions are matched and the clustering features, i.e., the number of triangles and the average clustering coefficient, are well reproduced.
Duennebeil, S.; Sunyaev, A.; Blohm, I.; Leimeister, J. M. & Krcmar, H.: Do German physicians want electronic health services? A characterization of potential adopters and rejectors in German ambulatory care. 3. International Conference on Health Informatics (HealthInf) 2010. Valencia, Spain: 2010
Tango, T.: Statistical Methods for Disease Clustering. 1. Aufl. New York, NY: Springer New York, 2010Statistics for Biology and Health
The development of powerful computing environment and the geographical information system (GIS) in recent decades has thrust the analysis of geo-referenced disease incidence data into the mainstream of spatial epidemiology. This book offers a modern perspective on statistical methods for detecting disease clustering, an indispensable procedure to find a statistical evidence on aetiology of the disease under study.
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
Bade, K.: Personalized Hierarchical Structuring. Otto-von-Guericke-Universitat Magdeburg, 2009
Carpineto, C.; Osiński, S.; Romano, G. & Weiss, D.: A survey of Web clustering engines. In: ACM Comput. Surv. 41 (2009), S. 17:1-17:38
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.
Lu, C.; Chen, X. & Park, E. K.: Exploit the tripartite network of social tagging for web clustering. Proceeding of the 18th ACM conference on Information and knowledge management. New York, NY, USA: ACM, 2009CIKM '09 , S. 1545-1548
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.
Nedjah, N.: Intelligent text categorization and clustering. Berlin, 2009
"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.
Leicht, E. A. & Newman, M. E. J.: Community Structure in Directed Networks. In: Phys. Rev. Lett. 100 (2008), Nr. 11, S. 118703
Brandes, U.; Delling, D.; Gaertler, M.; Görke, R.; Hoefer, M.; Nikoloski, Z. & Wagner, D.: On Finding Graph Clusterings with Maximum Modularity. In: Brandstädt, A.; Kratsch, D. & Müller, H. (Hrsg.): Graph-Theoretic Concepts in Computer Science. Berlin / Heidelberg: Springer, 2007 (Lecture Notes in Computer Science 4769), S. 121-132
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.
Grahl, M.; Hotho, A. & Stumme, G.: Conceptual Clustering of Social Bookmark Sites. In: Hinneburg, A. (Hrsg.): Workshop Proceedings of Lernen - Wissensentdeckung - Adaptivität (LWA 2007). Martin-Luther-Universität Halle-Wittenberg, 2007, S. 50-54
Newman, M. E. J.: Modularity and community structure in networks. In: Proceedings of the National Academy of Sciences 103 (2006), Nr. 23, S. 8577-8582
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.
Schmitz, C.; Hotho, A.; Jäschke, R. & Stumme, G.: Content Aggregation on Knowledge Bases using Graph Clustering. In: Sure, Y. & Domingue, J. (Hrsg.): The Semantic Web: Research and Applications. Heidelberg: Springer, 2006 (LNAI 4011), S. 530-544
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.
Dhillon, I. S.; Modha, D. S. & Spangler, W. S.: Class visualization of high-dimensional data with applications. In: Computational Statistics & Data Analysis 41 (2002), Nr. 1, S. 59-90
No abstract is available for this item.
Stumme, G.; Taouil, R.; Bastide, Y. & Lakhal, L.: Conceptual Clustering with Iceberg Concept Lattices. In: Klinkenberg, R.; Rüping, S.; Fick, A.; Henze, N.; Herzog, C.; Molitor, R. & Schröder, O. (Hrsg.): Proc. GI-Fachgruppentreffen Maschinelles Lernen (FGML'01). Universität Dortmund 763: 2001
Toivonen, J.; Visa, A.; Vesanen, T.; Back, B. & Vanharanta, H.: Validation of Text Clustering Based on Document Contents.. In: Perner, P. (Hrsg.): MLDM. Springer, 2001 (Lecture Notes in Computer Science 2123), S. 184-195
Jain, A. K.; Murty, M. N. & Flynn, P. J.: Data Clustering: A Review. In: ACM Comput. Surv. 31 (1999), Nr. 3, S. 264-323
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.
Jain, A. K.; Murty, M. N. & Flynn, P. J.: Data clustering: a review. In: ACM Comput. Surv. 31 (1999), Nr. 3, S. 264-323
Zhang, T.; Ramakrishnan, R. & Livny, M.: BIRCH: An Efficient Data Clustering Method for Very Large Databases. Proceedings of the 1996 ACM SIGMOD International Conference on Management of Data. New York, NY, USA: ACM, 1996SIGMOD '96 , S. 103-114