Academic literature on the topic 'Clustern'

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Journal articles on the topic "Clustern"

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Ahamad, Mohammed Gulam, Mohammed Faisal Ahmed, and Mohammed Yousuf Uddin. "Clustering as Data Mining Technique in Risk Factors Analysis of Diabetes, Hypertension and Obesity." European Journal of Engineering and Technology Research 1, no. 6 (2018): 88–93. http://dx.doi.org/10.24018/ejeng.2016.1.6.202.

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This investigation explores data mining using open source software WEKA in health care application. The cluster analysis technique is utilized to study the effects of diabetes, obesity and hypertension from the database obtained from Virginia school of Medicine. The simple k-means cluster techniques are adopted to form ten clusters which are clearly discernible to distinguish the differences among the risk factors such as diabetes, obesity and hypertension. Cluster formation was tried by trial and error method and also kept the SSE as low as possible. The SSE is low when numbers of clusters ar
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Völker, Sebastian. "Geografische Cluster der Unterimmunisierung gegen Influenza bei über 60-Jährigen am Beispiel Westfalen-Lippe." Das Gesundheitswesen 82, no. 05 (2019): 413–21. http://dx.doi.org/10.1055/a-0921-7374.

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Zusammenfassung Hintergrund Die Unterimmunisierung gegen Influenza steigt in den vergangenen Jahren an. Es wird eine räumliche Clusterung der Unterimmunisierung vermutet. Diese Cluster können Risiken für die Gesundheitsvorsorge bedeuten und das Erreichen von Qualitätsstandards der Immunisierung erschweren. Ziele des vorliegenden Papers sind (a) räumliche Cluster auf PLZ-Ebene mit hohen Quoten der Unterimmunisierung gegen Influenza zu identifizieren und zu beschreiben, (b) die Cluster mit weiteren Vorsorgeleistungen zu vergleichen und (c) mögliche Einflussfaktoren der Unterimmunisierung zu mode
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Scheuplein, Christoph. "Identifizierung und Analyse von Produktionsclustern." Raumforschung und Raumordnung 60, no. 2 (2002): 123–35. http://dx.doi.org/10.1007/bf03183868.

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KurzfassungObwohl sich „Cluster-Ansätze” in der Regionalpolitik zunehmender Beliebtheit erfreuen, sind die Methoden zur Beschreibung und Analyse von Produktionsclustern bislang nicht zufriedenstellend. Es wird ein Verfahren vorgestellt, mit dem Cluster sachlich und räumlich abgegrenzt werden können, die interne Verflechtung seiner Akteure aufgezeigt und ein interregionaler Vergleich von Clustern vorgenommen werden kann. Hierzu werden regionalstatistische Methoden mit einer Befragung von Cluster-Akteuren kombiniert. Die Auswertung zeigt zum einen, wie die Akteure in den funktionalen Zusammenhan
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Jahn, Robert W., Mathias Götzl, and Thilo J. Ketschau. "Lernen in variablen Clustern." Zeitschrift für Berufsund Wirtschaftspädagogik 116, no. 4 (2020): 549. http://dx.doi.org/10.25162/zbw-2020-0024.

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Fischer, M. "Vernetzung von Clustern." PIK - Praxis der Informationsverarbeitung und Kommunikation 24, no. 3 (2001): 149–51. http://dx.doi.org/10.1515/piko.2001.149.

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Duschek, Stephan, Frank Lerch, and Jörg Sydow. "Netzwerkberatung in Clustern." Gruppendynamik und Organisationsberatung 41, no. 2 (2010): 125–43. http://dx.doi.org/10.1007/s11612-010-0107-6.

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Hartke, Bernd. "Strukturübergänge in Clustern." Angewandte Chemie 114, no. 9 (2002): 1534–54. http://dx.doi.org/10.1002/1521-3757(20020503)114:9<1534::aid-ange1534>3.0.co;2-s.

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Reisinger, Manfred, Florian Knasmüller, Jan Aden, and Brigitte Sindelar. "Herausforderungen in der psychosozialen Versorgung am Beispiel der psychotherapeutischen Universitätsambulanz der Sigmund Freud PrivatUniversität – Eine Klientelanalyse." Psychotherapie Forum 25, no. 1-2 (2021): 54–61. http://dx.doi.org/10.1007/s00729-021-00176-3.

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ZusammenfassungDas Versorgungsmodell der Universitätsambulanz der SFU folgt der Zielsetzung, einen niederschwelligen Zugang zur Psychotherapie zu eröffnen, indem niedrige Tarife und kurze Wartezeiten für die Patient*innen die Regel darstellen. Um die Klientelstruktur der Ambulanz systematisch beschreiben zu können, wurden über einen Erhebungszeitraum von 2015–2018 Patient*innenmerkmale einer Untersuchungsstichprobe von n = 629 Patient*innen erhoben. Auf Basis soziodemographischer und krankengeschichtlicher Informationen sowie der aktuellen Lebensqualität (WHOQOL) wurde eine Clusteranalyse nach
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Hoch, Constantin, Marco Wendorff, and Caroline Röhr. "Alkalimetalltetrelide mit M94−-Clustern." Zeitschrift für anorganische und allgemeine Chemie 628, no. 9-10 (2002): 2172. http://dx.doi.org/10.1002/1521-3749(200209)628:9/10<2172::aid-zaac2172>3.0.co;2-8.

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Jaenicke, Lothar. "Stickstoffreduktion mit Titan-Clustern." Chemie in unserer Zeit 48, no. 1 (2014): 10. http://dx.doi.org/10.1002/ciuz.201490000.

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Dissertations / Theses on the topic "Clustern"

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Dahmen, Klaus-Hermann. "Bildung von heterometallischen Clustern : eine koordinationschemische Untersuchung von Platin-Carbonyl-Phosphin-Clustern /." [S.l.] : [s.n.], 1986. http://e-collection.ethbib.ethz.ch/show?type=diss&nr=8172.

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Gerhardt, Peter Wenzel. "Schnelle Relaxation von Clustern." [S.l.] : [s.n.], 2004. http://deposit.ddb.de/cgi-bin/dokserv?idn=972080589.

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Scholz, Axel. "Echtzeit-Untersuchungen an Natrium-Ammoniak-Clustern." [S.l. : s.n.], 1998. http://www.diss.fu-berlin.de/1999/5/index.html.

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Jansen, Andreas. "Mehrdimensionale, anharmonische Schwingungsanalyse von wasserstoffbrückengebundenen Clustern." [S.l. : s.n.], 2003. http://deposit.ddb.de/cgi-bin/dokserv?idn=968579493.

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Barth, Silko. "Laserdiagnostik an elektrodynamisch gespeicherten ligandenstabilisierten Clustern." [S.l.] : Tech. Univ. Chemnitz, Fakultät für Naturwissenschaften, 2000. http://www.bsz-bw.de/cgi-bin/xvms.cgi?SWB10607515.

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Fischer, Tim. "Untersuchung von massenselektierten Clustern mittels VUV-Lasern Photoelektronenspektroskopie massenselektierter Cluster mit Lasern extrem kurzer Wellenlänge /." [S.l. : s.n.], 2008. http://nbn-resolving.de/urn:nbn:de:bsz:352-opus-66897.

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Brause, Robert. "Franck-Condon-Analyse an Heteroaromaten und Clustern." [S.l.] : [s.n.], 2006. http://deposit.ddb.de/cgi-bin/dokserv?idn=979718503.

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Farmanara, Parviz. "Femtosekunden-Laserspektroskopie an molekularen Modellsystemen und Clustern." [S.l.] : [s.n.], 2001. http://www.diss.fu-berlin.de/2001/103/index.html.

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Rabenhorst, Hans-Christian. "Service-Erwartungen und Leistungsbündel in Chemie-Clustern." [Düsseldorf] Dup, 2008. http://d-nb.info/991992288/04.

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Kabrede, Hendrik. "Energieoptimierung von Clustern und spinodale Entmischung in Fluiden." [S.l.] : [s.n.], 2004. http://deposit.ddb.de/cgi-bin/dokserv?idn=973072199.

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Books on the topic "Clustern"

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Beyer, David. Kooperationserfolg in Clustern mit Netzwerkcharakter. Springer Fachmedien Wiesbaden, 2016. http://dx.doi.org/10.1007/978-3-658-13926-1.

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Reichwein, Lars. Struktur von Coulomb-Clustern im Bubble-Regime. Springer Fachmedien Wiesbaden, 2020. http://dx.doi.org/10.1007/978-3-658-28898-3.

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Terstriep, Judith. Bedeutung von Clustern für die Innovativität von Unternehmen. Springer Fachmedien Wiesbaden, 2019. http://dx.doi.org/10.1007/978-3-658-27818-2.

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Berlin, Freie Universität, ed. Matrixspektroskopische Untersuchung der Wechselwirkung von CO mit Silber-Clustern. [s.n.], 1996.

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Reetz, Manfred T., and Heinz Mehlhorn. Größenselektive Synthese von Nanostrukturierten Metall-Clustern. Parasiten: Ihre Bedeutung heute. VS Verlag für Sozialwissenschaften, 1997. http://dx.doi.org/10.1007/978-3-663-01727-1.

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Rimkus, Manuel. Wissenstransfer in Clustern: Eine Analyse am Beispiel des Biotech-Standorts Martinsried. Gabler, 2008.

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Hannover, Universität, ed. Synthese und Charakterisierung neuer, reduzierter Halogenide der Lanthanide mit homo- und heterointerstitiellen Clustern. [s.n.], 1996.

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1956-, Schmid Josef, Heinze Rolf G. 1951-, and Beck Rasmus C, eds. Strategische Wirtschaftsförderung und die Gestaltung von High-Tech Clustern: Beiträge zu den Chancen und Restriktionen von Clusterpolitik. Nomos, 2009.

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Schladerbeck, Norbert H. Zur Darstellung von Übergangsmetall-Schwefel-Clustern mit H₂S [HS] als Schwefeldonor in Gegenwart von O₂ [O]. [s.n.], 1988.

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J, Reynolds Peter, ed. On clusters and clustering: From atoms to fractals. North-Holland, 1993.

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Book chapters on the topic "Clustern"

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Fröhlich, Lutz. "Replikation zwischen Clustern." In PostgreSQL 10. Carl Hanser Verlag GmbH & Co. KG, 2018. http://dx.doi.org/10.3139/9783446456419.009.

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Joos, Thomas. "Cluster erstellen und erste Schritte mit vSphere-Clustern." In VMware vSphere 6.5. Carl Hanser Verlag GmbH & Co. KG, 2017. http://dx.doi.org/10.3139/9783446452978.005.

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Brode, Stefan. "Quantenchemie auf Workstation-Clustern." In Supercomputer ’93. Springer Berlin Heidelberg, 1993. http://dx.doi.org/10.1007/978-3-642-78348-7_15.

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Beyer, David. "Einleitung." In Kooperationserfolg in Clustern mit Netzwerkcharakter. Springer Fachmedien Wiesbaden, 2016. http://dx.doi.org/10.1007/978-3-658-13926-1_1.

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Beyer, David. "Grundlagen zur Erklärung von Kooperationserfolg in Clustern." In Kooperationserfolg in Clustern mit Netzwerkcharakter. Springer Fachmedien Wiesbaden, 2016. http://dx.doi.org/10.1007/978-3-658-13926-1_2.

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Beyer, David. "Forschungsstand zu Kooperationserfolg in Clustern und Netzwerken." In Kooperationserfolg in Clustern mit Netzwerkcharakter. Springer Fachmedien Wiesbaden, 2016. http://dx.doi.org/10.1007/978-3-658-13926-1_3.

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Beyer, David. "Entwicklung eines Modells zur empirischen Untersuchung von Kooperationserfolg in Clustern mit Netzwerkcharakter." In Kooperationserfolg in Clustern mit Netzwerkcharakter. Springer Fachmedien Wiesbaden, 2016. http://dx.doi.org/10.1007/978-3-658-13926-1_4.

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Beyer, David. "Empirische Untersuchung der Erfolgswirkung von Koordinationsmechanismen in Clustern mit Netzwerkcharakter." In Kooperationserfolg in Clustern mit Netzwerkcharakter. Springer Fachmedien Wiesbaden, 2016. http://dx.doi.org/10.1007/978-3-658-13926-1_5.

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Beyer, David. "Interpretation der Ergebnisse und Handlungsempfehlungen." In Kooperationserfolg in Clustern mit Netzwerkcharakter. Springer Fachmedien Wiesbaden, 2016. http://dx.doi.org/10.1007/978-3-658-13926-1_6.

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Beyer, David. "Zusammenfassung und Ausblick." In Kooperationserfolg in Clustern mit Netzwerkcharakter. Springer Fachmedien Wiesbaden, 2016. http://dx.doi.org/10.1007/978-3-658-13926-1_7.

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Conference papers on the topic "Clustern"

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Bubenik, Thomas A., Steven J. Polasik, and Benjamin Hanna. "Impact of Corrosion Growth on Effective Flaw Dimensions." In CONFERENCE 2024. AMPP, 2024. https://doi.org/10.5006/c2024-21001.

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Abstract This paper summarizes the results of a sensitivity study conducted on corrosion clusters on a steel pipeline, specifically (1) the change in cluster effective length as the individual anomalies that make up the cluster grow and (2) the effect of changes in effective length on the remaining life calculated using deterministic methods. Two growth scenarios are considered: clusters that grow at the same rate along the cluster versus clusters that grow at a depth-based corrosion growth rate (e.g., deeper areas grow faster than shallower areas). The study shows that, in both scenarios, the
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Katzenstein, Sara, Arvid Sandforth, Vitória Minelli-Faiao, et al. "Entzündungsmediatoren in neuen Clustern von Individuen mit erhöhtem Diabetesrisiko." In Diabetes Kongress 2023 - 57. Jahrestagung der DDG. Georg Thieme Verlag, 2023. http://dx.doi.org/10.1055/s-0043-1767847.

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Katzenstein, Sarah, Arvid Sandforth, Vitória Minelli-Faiao, et al. "Remissionsraten nach Lebensstilintervention in spezifischen Hoch-Risiko Prädiabetes-Clustern." In Diabetes. Umwelt. Leben. Perspektiven aus allen Blickwinkeln. Georg Thieme Verlag KG, 2024. http://dx.doi.org/10.1055/s-0044-1785418.

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Buch, Amanda, Conor Liston, and Logan Grosenik. "Cluster-Aware Algorithms for AI-Enabled Precision Medicine." In LatinX in AI at Neural Information Processing Systems Conference 2023. Journal of LatinX in AI Research, 2023. http://dx.doi.org/10.52591/lxai2023121011.

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AI-enabled precision medicine promises a transformative improvement in healthcare outcomes. However, training on biomedical data presents a challenge: such data are often high dimensional, clustered, and of limited sample size. To overcome this, we propose a simple and scalable approach for cluster-aware embedding that augments embedding methods with a convex clustering penalty. This approach outperforms fourteen widely-used clustering methods on both highly underdetermined problems and on large sample datasets, yielding interpretable dendrograms of the embedded clusters. Our approach improves
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Zhao, Han, Xu Yang, Zhenru Wang, Erkun Yang, and Cheng Deng. "Graph Debiased Contrastive Learning with Joint Representation Clustering." In Thirtieth International Joint Conference on Artificial Intelligence {IJCAI-21}. International Joint Conferences on Artificial Intelligence Organization, 2021. http://dx.doi.org/10.24963/ijcai.2021/473.

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By contrasting positive-negative counterparts, graph contrastive learning has become a prominent technique for unsupervised graph representation learning. However, existing methods fail to consider the class information and will introduce false-negative samples in the random negative sampling, causing poor performance. To this end, we propose a graph debiased contrastive learning framework, which can jointly perform representation learning and clustering. Specifically, representations can be optimized by aligning with clustered class information, and simultaneously, the optimized representatio
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Venkataraman, P. "Data Clustering Using the Natural Bézier Functions." In ASME 2018 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2018. http://dx.doi.org/10.1115/detc2018-85103.

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An unorthodox and effective non-iterative procedure for spherical clusters is demonstrated in this paper. It uses natural Bézier functions to determine initial cluster locations using the content of the data. The natural Bernstein-Bézier functions are very robust in representing data through continuous functions in the application of functional data analysis. This paper demonstrates that they are equally robust at resolving data clusters in classification problems. The original data is scaled and segmented. A natural Bézier function is fitted for each segment and the initial clusters are cente
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Pancaroglu, Doruk. "Artist, Style and Year Classification using Face Recognition and Clustering with Convolutional Neural Networks." In 4th International Conference on Computer Science and Information Technology (COMIT 2020). AIRCC Publishing Corporation, 2020. http://dx.doi.org/10.5121/csit.2020.101604.

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Artist, year and style classification of fine-art paintings are generally achieved using standard image classification methods, image segmentation, or more recently, convolutional neural networks (CNNs). This works aims to use newly developed face recognition methods such as FaceNet that use CNNs to cluster fine-art paintings using the extracted faces in the paintings, which are found abundantly. A dataset consisting of over 80,000 paintings from over 1000 artists is chosen, and three separate face recognition and clustering tasks are performed. The produced clusters are analyzed by the file n
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Suchacka, Grazyna. "Time series clustering with different distance measures to tell Web bots and humans apart." In 36th ECMS International Conference on Modelling and Simulation. ECMS, 2022. http://dx.doi.org/10.7148/2022-0303.

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The paper deals with the problem of differentiating Web sessions of bots and human users by observing some characteristics of their traffic at the Web server input. We propose an approach to cluster bots’ and humans’ sessions represented as time series. First, sessions are expressed as sequences of HTTP requests coming to the server at specific timestamps; then, they are pre-preprocessed to form time series of limited length. Time series are clustered and the clustering performance is evaluated in terms of the ability to partition bots and humans into separate clusters. The proposed approach i
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Zhang, Hongjing, and Ian Davidson. "Deep Descriptive Clustering." In Thirtieth International Joint Conference on Artificial Intelligence {IJCAI-21}. International Joint Conferences on Artificial Intelligence Organization, 2021. http://dx.doi.org/10.24963/ijcai.2021/460.

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Recent work on explainable clustering allows describing clusters when the features are interpretable. However, much modern machine learning focuses on complex data such as images, text, and graphs where deep learning is used but the raw features of data are not interpretable. This paper explores a novel setting for performing clustering on complex data while simultaneously generating explanations using interpretable tags. We propose deep descriptive clustering that performs sub-symbolic representation learning on complex data while generating explanations based on symbolic data. We form good c
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Cheng, Zhanwen, Feijiang Li, Jieting Wang, and Yuhua Qian. "Deep Embedding Clustering Driven by Sample Stability." In Thirty-Third International Joint Conference on Artificial Intelligence {IJCAI-24}. International Joint Conferences on Artificial Intelligence Organization, 2024. http://dx.doi.org/10.24963/ijcai.2024/426.

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Deep clustering methods improve the performance of clustering tasks by jointly optimizing deep representation learning and clustering. While numerous deep clustering algorithms have been proposed, most of them rely on artificially constructed pseudo targets for performing clustering. This construction process requires some prior knowledge, and it is challenging to determine a suitable pseudo target for clustering. To address this issue, we propose a deep embedding clustering algorithm driven by sample stability (DECS), which eliminates the requirement of pseudo targets. Specifically, we start
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Reports on the topic "Clustern"

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Kryzhanivs'kyi, Evstakhii, Liliana Horal, Iryna Perevozova, Vira Shyiko, Nataliia Mykytiuk, and Maria Berlous. Fuzzy cluster analysis of indicators for assessing the potential of recreational forest use. [б. в.], 2020. http://dx.doi.org/10.31812/123456789/4470.

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Cluster analysis of the efficiency of the recreational forest use of the region by separate components of the recreational forest use potential is provided in the article. The main stages of the cluster analysis of the recreational forest use level based on the predetermined components were determined. Among the agglomerative methods of cluster analysis, intended for grouping and combining the objects of study, it is common to distinguish the three most common types: the hierarchical method or the method of tree clustering; the K-means Clustering Method and the two-step aggregation method. For
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Kim-Sherman, Jacob, and Lee Seltzer. Clustering in Natural Disaster Losses. Federal Reserve Bank of New York, 2024. http://dx.doi.org/10.59576/sr.1135.

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In contrast with findings in climate science, economists often treat losses from natural disasters as statistically independent of one another. To better incorporate scientific insights into economic research, we introduce a methodology to identify spatial and temporal clusters in datasets on losses from natural disasters. We find that expected damage increases non-linearly with relative cluster size. Additionally, county-level damage is correlated with the damage experienced by other counties in the same cluster. Our findings suggest that accounting for clustering allows for a more complete u
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Rabellotti, Roberta, and Elisa Conz. Mainstreaming the Cluster Approach in the Caribbean Region: The CCPF Cluster Capacity Building Initiative. Inter-American Development Bank, 2023. http://dx.doi.org/10.18235/0004771.

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In the Caribbean region, clusters hold tremendous potential because of the predominance of small and micro enterprises, their weak link in value chains and the scarcity of specialized inputs. The Compete Caribbean Partnership Facility (CCPF) has backed a capacity-building campaign for the cluster approach with the aim of mainstreaming cluster interventions in the region. This report explores how the process of mainstreaming is taking shape among those business support organizations (BSOs) involved in the CCPF project, currently involved in the implementation of cluster projects. The study addr
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Monteiro, Joana, Beatriz Kury, and Angela Da Rocha. The Role of Flagship Firms, External Actors and Support Institutions in the Emergence of Successful Export Activities in Brazil: Two Industrial Cluster Studies. Inter-American Development Bank, 2009. http://dx.doi.org/10.18235/0011331.

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This paper examines the process by which firms in a cluster start to export based on systemic interactions and the process of diffusion of exporting as a business strategy within the cluster. Two Brazilian manufacturing industries are studied, and within each one a geographic cluster was identified as the origin of dynamic export growth. Players in each industrial cluster, as well as other significant players, were interviewed or identified using secondary sources, and extensive secondary data research was undertaken to study clusters' historical development. Detailed analysis and a comparison
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Ramis, Luis. Políticas de clusters: Análisis de clusters en Mendoza: Objetivos, mapping y selección de clusters, recomendaciones. Inter-American Development Bank, 2005. http://dx.doi.org/10.18235/0007265.

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Presentación sobre el análisis de los clusters en Mendoza. Da a conocer los objetivos y alcance del proyecto, mapping de clusters y selección del piloto, la metodología de mejora de la competitividad en un cluster y por último, las recomendaciones para una política de competitividad basada en clusters.
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Rodríguez-Clare, Andrés. Clusters and Comparative Advantage: Implications for Industrial Policy. Inter-American Development Bank, 2005. http://dx.doi.org/10.18235/0010960.

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Industrial agglomerations or 'clusters' arise in the presence of industry-specific and local externalities, also called Marshallian externalities. The standard argument is that such externalities may justify a policy of infant-industry protection to allow and encourage clusters to emerge. This paper explores that argument and shows that different policy implications emerge under a more realistic modeling of clusters. In particular, rather than distorting prices to promote clusters in 'advanced' sectors that may exhibit strong clustering possibilities, countries should focus instead on promotin
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Iurasova, Olga, Larysa Ivashko, Oleksandr Maksymov, and Julia Maksymova. Impact of Return on Education on Economic Growth in EU Countries. Vilnius Business College, 2024. https://doi.org/10.57005/ab.2024.2.6.

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The objective of this article is to assess the impact of returns on the education and professional skills of workers on economic growth in EU countries. Based on open data, two principal components were formed to identify the aggregated influence of selected indicators on GDP growth. These principal components allow for the evaluation of the degree of influence of education and professional skills of workers on GDP growth for each country. Countries were clustered according to the degree of influence of the obtained principal components on the level of economic development using the k-means me
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Raykov, Tenko. Latent Class Analysis and Mixture Modeling. Instats Inc., 2023. http://dx.doi.org/10.61700/tkd5fah8evykd469.

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Latent class analysis (LCA) and mixture models (MM) are an applied statistical method for examining heterogeneity in studied populations. The method can be used to evaluate whether a studied population consists of an initially unknown number of several subpopulations (latent classes, types, clusters) that differ in important ways. This workshop introduces participants to the general field of classification (clustering), using LCA as a model-based version of cluster analysis and moving on to more general mixture modeling with latent variables. Hands-on examples with best practices for analysis
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Owen, R. K. Quantum Monte Carlo methods and lithium cluster properties. [Atomic clusters]. Office of Scientific and Technical Information (OSTI), 1990. http://dx.doi.org/10.2172/7204421.

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Campi, Mercedes, and Marco Dueñas. Clusters and Resilience during the COVID–19 Crisis: Evidence from Colombian Exporting Firms. Inter-American Development Bank, 2022. http://dx.doi.org/10.18235/0004474.

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In this paper, we characterize the geography of Colombian exporting clusters and analyze how the COVID-19 crisis has affected Colombian exporters. We contribute to the industrial clusters literature by defining exporting clusters with bipartite network analysis and community detection tools. The methodology allows us to empirically detect product clusters, which are compared with an alternative definition of industrial clusters, and to consider the centrality of firms within clusters. Then, we analyze the firms trade margins during the COVID-19 crisis to evaluate whether belonging to an export
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