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Littérature scientifique sur le sujet « Model-based Cluster »

Auteur : Grafiati

Publié le 9 mars 2023

Mis à jour le 31 juillet 2025

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Articles de revues sur le sujet "Model-based Cluster"

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Endo, Yasunori, Ayako Heki, and Yukihiro Hamasuna. "Non Metric Model Based on Rough Set Representation." Journal of Advanced Computational Intelligence and Intelligent Informatics 17, no. 4 (2013): 540–51. http://dx.doi.org/10.20965/jaciii.2013.p0540.

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The non metricmodel is a kind of clustering method in which belongingness or the membership grade of each object in each cluster is calculated directly from dissimilarities between objects and in which cluster centers are not used. The clustering field has recently begun to focus on rough set representation instead of fuzzy set representation. Conventional clustering algorithms classify a set of objects into clusters with clear boundaries, that is, one object must belong to one cluster. Many objects in the real world, however, belong to more than one cluster because cluster boundaries overlap

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Duan, Guiqin, and Chensong Zou. "A clustering effectiveness measurement model based on merging similar clusters." PeerJ Computer Science 10 (February 29, 2024): e1863. http://dx.doi.org/10.7717/peerj-cs.1863.

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This article presents a clustering effectiveness measurement model based on merging similar clusters to address the problems experienced by the affinity propagation (AP) algorithm in the clustering process, such as excessive local clustering, low accuracy, and invalid clustering evaluation results that occur due to the lack of variety in some internal evaluation indices when the proportion of clusters is very high. First, depending upon the “rough clustering” process of the AP clustering algorithm, similar clusters are merged according to the relationship between the similarity between any two

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Banerjee, Saibal, and Azriel Rosenfeld. "Model-based cluster analysis." Pattern Recognition 26, no. 6 (1993): 963–74. http://dx.doi.org/10.1016/0031-3203(93)90061-z.

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Stahl, Daniel, and Hannah Sallis. "Model-based cluster analysis." Wiley Interdisciplinary Reviews: Computational Statistics 4, no. 4 (2012): 341–58. http://dx.doi.org/10.1002/wics.1204.

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Huang, He, and Hui Xiao. "Internet Industry Cluster Design Based on PDE Mathematical Model." Applied Mechanics and Materials 539 (July 2014): 959–63. http://dx.doi.org/10.4028/www.scientific.net/amm.539.959.

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The industrial cluster is formed by the common competitiveness elements of enterprise group. Under the cluster environment, common technology and common customer as well as distribution channel are composition of cluster development performance mode. On the basis of the parabolic PDE cluster development model, and combined with Internet industrial cluster analysis of virtual platform, the Internet structure industrial cluster analysis system is designed. In order to verify the validity and reliability of the model and system, this paper takes the cluster development of machining as an example

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Younghwan Kim, Younghwan Kim, and Huy Kang Kim Younghwan Kim. "Cluster-based Deep One-Class Classification Model for Anomaly Detection." 網際網路技術學刊 22, no. 4 (2021): 903–11. http://dx.doi.org/10.53106/160792642021072204017.

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Fang, Yong Heng, and Jing Yi Yi. "Study on Evolution Mechanism of Industrial Cluster Based on Brusselator Model." Applied Mechanics and Materials 687-691 (November 2014): 4832–35. http://dx.doi.org/10.4028/www.scientific.net/amm.687-691.4832.

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The article using Brusselator model analyses the evolution mechanism of industrial clusters. The study found, the formation of industrial clusters is an inner reinforcing cycle accumulation process, the competing interaction is an important condition for the evolution of industrial clusters, and cluster innovation driving the system to the state development more orderly, form the new dissipative structure, promote the evolution of industrial cluster.

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Meghana, J., J. Hanumanthappa, S. P. Shiva Prakash, and Kiril Krinkin. "Relationship-Cluster Head Selection and Data Compression Enabled Cluster-Based Aggregation Model for Social Internet of Things." Indian Journal Of Science And Technology 16, no. 41 (2023): 3605–16. http://dx.doi.org/10.17485/ijst/v16i41.1256.

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Lim, Michael K., and So Young Sohn. "Cluster-based dynamic scoring model." Expert Systems with Applications 32, no. 2 (2007): 427–31. http://dx.doi.org/10.1016/j.eswa.2005.12.006.

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Lahoorpoor, Bahman, Hamed Faroqi, Abolghasem Sadeghi-Niaraki, and Soo-Mi Choi. "Spatial Cluster-Based Model for Static Rebalancing Bike Sharing Problem." Sustainability 11, no. 11 (2019): 3205. http://dx.doi.org/10.3390/su11113205.

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Bike sharing systems, as one of the complementary modes for public transit networks, are designed to help travelers in traversing the first/last mile of their trips. Different factors such as accessibility, availability, and fares influence these systems. The availability of bikes at certain times and locations is studied under rebalancing problem. The paper proposes a bottom-up cluster-based model to solve the static rebalancing problem in bike sharing systems. First, the spatial and temporal patterns of bike sharing trips in the network are investigated. Second, a similarity measure based on

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Thèses sur le sujet "Model-based Cluster"

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Rapley, Veronica Elizabeth. "Model-based adaptive cluster sampling." Thesis, University of Southampton, 2005. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.433939.

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Lin, Dong. "Model-based cluster analysis using Bayesian techniques." To access this resource online via ProQuest Dissertations and Theses @ UTEP, 2008. http://0-proquest.umi.com.lib.utep.edu/login?COPT=REJTPTU0YmImSU5UPTAmVkVSPTI=&clientId=2515.

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Tantrum, Jeremy. "Model based and hybrid clustering of large datasets /." Thesis, Connect to this title online; UW restricted, 2003. http://hdl.handle.net/1773/8933.

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BARBERIS, STEFANO. "New developments in Cluster-Weighted Modeling." Doctoral thesis, Università degli Studi di Milano-Bicocca, 2019. http://hdl.handle.net/10281/241157.

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In questo lavoro vengono presentate due estensioni del modello Cluster Weighted (un modello mistura di regressioni che considera non solo la distribuzione condizionata ma anche la distribuzione marginale delle covariate). La prima estensione proposta presenta il modello GAM-CWM. Si tratta di un modello molto flessibile, in grado di descrivere relazioni complesse tra una variabile risposta e un insieme di covariate in ogni componente della mistura. In questo modello proposto la classe dei Generalized Additive Models è stata inserita all’interno del framework CWM. La seconda estensione, è

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Annakula, Chandravyas. "Hierarchical and partitioning based hybridized blocking model." Kansas State University, 2017. http://hdl.handle.net/2097/35468.

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Master of Science<br>Department of Computing and Information Sciences<br>William H. Hsu<br>(Higgins, Savje, & Sekhon, 2016) Provides us with a sampling blocking algorithm that enables large and complex experiments to run in polynomial time without sacrificing the precision of estimates on a covariate dataset. The goal of this project is to run the different clustering algorithms on top of clusters formed from above mentioned blocking algorithm and analyze the performance and compatibility of the clustering algorithms. We first start with applying the blocking algorithm on a covariate dataset

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Mohamed, Esha [Verfasser], and Ralf [Akademischer Betreuer] Münnich. "Design-based and model-based estimation in adaptive cluster sampling / Esha Mohamed ; Betreuer: Ralf Münnich." Trier : Universität Trier, 2017. http://d-nb.info/1197807535/34.

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Malsiner-Walli, Gertraud, Sylvia Frühwirth-Schnatter, and Bettina Grün. "Model-based clustering based on sparse finite Gaussian mixtures." Springer, 2016. http://dx.doi.org/10.1007/s11222-014-9500-2.

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In the framework of Bayesian model-based clustering based on a finite mixture of Gaussian distributions, we present a joint approach to estimate the number of mixture components and identify cluster-relevant variables simultaneously as well as to obtain an identified model. Our approach consists in specifying sparse hierarchical priors on the mixture weights and component means. In a deliberately overfitting mixture model the sparse prior on the weights empties superfluous components during MCMC. A straightforward estimator for the true number of components is given by the most frequent number

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Shaffer, Anne, Monica Whitehead, Molly Davis, Diana Morelen, and Cynthia Suveg. "A Model‐Based Cluster Analysis of Maternal Emotion Regulation and Relations to Parenting Behavior." Digital Commons @ East Tennessee State University, 2018. https://doi.org/10.1111/famp.12326.

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In a diverse community sample of mothers (N = 108) and their preschool‐aged children (Mage = 3.50 years), this study conducted person‐oriented analyses of maternal emotion regulation (ER) based on a multimethod assessment incorporating physiological, observational, and self‐report indicators. A model‐based cluster analysis was applied to five indicators of maternal ER: maternal self‐report, observed negative affect in a parent–child interaction, baseline respiratory sinus arrhythmia (RSA), and RSA suppression across two laboratory tasks. Model‐based cluster analyses revealed four maternal ER p

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Whitehead, Monica R., Anne Shaffer, Molly Faye Davis, Diana M. Morelen, and Cynthia Suveg. "A Model-Based Cluster Analysis of Maternal Emotion Regulation and Relations to Parenting Behavior." Digital Commons @ East Tennessee State University, 2017. https://dc.etsu.edu/etsu-works/745.

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Liu, Jinghui. "Approaches to improve the precision of similarity patterns and reproducibility for cluster analysis infinite mixture model based cluster analyses for gene expression data /." Cincinnati, Ohio : University of Cincinnati, 2008. http://rave.ohiolink.edu/etdc/view.cgi?acc_num=ucin1211903300.

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Livres sur le sujet "Model-based Cluster"

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Rojas, Thomas D. National forest economic clusters: A new model for assessing national-forest-based natural resources products and services. United States Dept. of Agriculture, Forest Service, Pacific Northwest Research Station, 2007.

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Rojas, Thomas D. National forest economic clusters: A new model for assessing national-forest-based natural resources products and services. U.S. Dept. of Agriculture, Forest Service, Pacific Northwest Research Station, 2007.

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Cluster-Based Industrial Development: An East Asian Model. Palgrave Macmillan, 2006.

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Otsuka, K., and Tetsushi Sonobe. Cluster-Based Industrial Development: An East Asian Model. Palgrave Macmillan, 2006.

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Mixture Model-Based Classification. Taylor & Francis Group, 2016.

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McNicholas, Paul D. Mixture Model-Based Classification. Taylor & Francis Group, 2016.

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McNicholas, Paul D. Mixture Model-Based Classification. Taylor & Francis Group, 2020.

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McNicholas, Paul D. Mixture Model-Based Classification. Taylor & Francis Group, 2016.

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McNicholas, Paul D. Mixture Model-Based Classification. Taylor & Francis Group, 2016.

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McNicholas, Paul D. Mixture Model-Based Classification. Taylor & Francis Group, 2016.

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Chapitres de livres sur le sujet "Model-based Cluster"

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Nizamani, Sarwat, Nasrullah Memon, and Uffe Kock Wiil. "Cluster Based Text Classification Model." In Lecture Notes in Social Networks. Springer Vienna, 2011. http://dx.doi.org/10.1007/978-3-7091-0388-3_14.

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Huang, He. "Cluster Enterprises’ Internationalization Based on Cluster Risk Evaluation Model." In Lecture Notes in Electrical Engineering. Springer London, 2013. http://dx.doi.org/10.1007/978-1-4471-4853-1_45.

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Sridevi, K. N., Surekha Pinnapati, and S. Prakasha. "Hierarchical Cluster-Based Model to Evaluate Accuracy Metrics Based on Cluster Efficiency." In Intelligent Sustainable Systems. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-6369-7_61.

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Bensmail, Halima, and Jacqueline J. Meulman. "MCMC Inference for Model-based Cluster analysis." In Studies in Classification, Data Analysis, and Knowledge Organization. Springer Berlin Heidelberg, 1998. http://dx.doi.org/10.1007/978-3-642-72253-0_26.

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García-Escudero, Luis A., Agustín Mayo-Iscar, Gianluca Morelli, and Marco Riani. "Old and New Constraints in Model Based Clustering." In Studies in Classification, Data Analysis, and Knowledge Organization. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-09034-9_16.

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AbstractModel-based approaches to cluster analysis and mixture modeling often involve maximizing classification and mixture likelihoods. Without appropriate constrains on the scatter matrices of the components, these maximizations result in ill-posed problems. Moreover, without constrains, non-interesting or “spurious” clusters are often detected by the EM and CEM algorithms traditionally used for the maximization of the likelihood criteria. A useful approach to avoid spurious solutions is to restrict relative components scatter by a prespecified tuning constant. Recently new methodologies for

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Kumar, Pardeep, Samit Barai, Babji Srinivasan, and Nihar R. Mohapatra. "Process Model Accuracy Enhancement Using Cluster Based Approach." In Physics of Semiconductor Devices. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-03002-9_9.

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Romoozi, Morteza, Hamideh Babaei, Mahmood Fathy, and Mojtaba Romoozi. "A Cluster-Based Mobility Model for Intelligent Nodes." In Computational Science and Its Applications – ICCSA 2009. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-02454-2_41.

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Li, Xiaotu, Jizhou Sun, Jiawan Zhang, Zhaohui Qi, and Gang Li. "A Modified Parallel Computation Model Based on Cluster." In Computational Science and Its Applications – ICCSA 2004. Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-24709-8_27.

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Barnat, Jiří, Luboš Brim, and Ivana Černá. "Cluster-Based LTL Model Checking of Large Systems." In Formal Methods for Components and Objects. Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11804192_13.

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Hennig, Christian, and Pietro Coretto. "The Noise Component in Model-based Cluster Analysis." In Data Analysis, Machine Learning and Applications. Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-78246-9_16.

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Actes de conférences sur le sujet "Model-based Cluster"

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Khan, Mozammel H. A. "Internal-Cluster-Validation-Based Model Selection For k-Means Clustering." In 2024 28th International Computer Science and Engineering Conference (ICSEC). IEEE, 2024. https://doi.org/10.1109/icsec62781.2024.10770636.

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Dash, Shitikantha, Ranjana Sodhi, and Sri Niwas Singh. "Cluster-Wise Response Aggregation-Based Differentially Private Pool Energy Market Model." In 2024 IEEE 4th International Conference on Sustainable Energy and Future Electric Transportation (SEFET). IEEE, 2024. http://dx.doi.org/10.1109/sefet61574.2024.10717938.

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Bamer, Balazs, Sabine Leroch, Andreas Hossinger, and Lado Filipovic. "Cluster-Based Semi-Empirical Model for Dopant Activation in Silicon Carbide." In 2024 International Conference on Simulation of Semiconductor Processes and Devices (SISPAD). IEEE, 2024. http://dx.doi.org/10.1109/sispad62626.2024.10732978.

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Sriprayoonsakul and Uthayopas. "An energy-based implicit co-scheduling model for Beowulf cluster." In Proceedings IEEE International Conference on Cluster Computing CLUSTR-03. IEEE, 2003. http://dx.doi.org/10.1109/clustr.2003.1253336.

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Akihiro Nomura, Hiroya Matsuba, and Yutaka Ishikawa. "Network performance model for TCP/IP based cluster computing." In 2007 IEEE International Conference on Cluster Computing (CLUSTER). IEEE, 2007. http://dx.doi.org/10.1109/clustr.2007.4629232.

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Gupta, Nikunj, Rohit Ashiwal, Bine Brank, Sateesh K. Peddoju, and Dirk Pleiter. "Performance Evaluation of ParalleX Execution model on Arm-based Platforms." In 2020 IEEE International Conference on Cluster Computing (CLUSTER). IEEE, 2020. http://dx.doi.org/10.1109/cluster49012.2020.00080.

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Wang, Shen-Ge. "Cluster-based binary printer model." In IS&T/SPIE Electronic Imaging, edited by Reiner Eschbach, Gabriel G. Marcu, Shoji Tominaga, and Alessandro Rizzi. SPIE, 2009. http://dx.doi.org/10.1117/12.811626.

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Yuan, Liang, and Yunquan Zhang. "A Locality-based Performance Model for Load-and-Compute Style Computation." In 2012 IEEE International Conference on Cluster Computing (CLUSTER). IEEE, 2012. http://dx.doi.org/10.1109/cluster.2012.25.

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Zhu, Niu, Lu, Shen, and Gao. "A cluster-based solution for high performance hmmpfam using EARTH execution model." In Proceedings IEEE International Conference on Cluster Computing CLUSTR-03. IEEE, 2003. http://dx.doi.org/10.1109/clustr.2003.1253296.

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Thonglek, Kundjanasith, Kohei Ichikawa, Keichi Takahashi, Hajimu Iida, and Chawanat Nakasan. "Improving Resource Utilization in Data Centers using an LSTM-based Prediction Model." In 2019 IEEE International Conference on Cluster Computing (CLUSTER). IEEE, 2019. http://dx.doi.org/10.1109/cluster.2019.8891022.

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Rapports d'organisations sur le sujet "Model-based Cluster"

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Ків, Арнольд Юхимович, D. Fuks, Наталя Володимирівна Моісеєнко, and Володимир Миколайович Соловйов. Silicon-aluminum bonding in Al alloys. Transport and Telecommunication Institute, 2002. http://dx.doi.org/10.31812/0564/1033.

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Ab initio calculation was performed to investigate the nature of Si-Al bonding in Al based alloys. Total electronic energy Etot for different configurations of the model cluster Si2Al6 was calculated. When the model cluster consists of two perfect tetrahedrons there is a strong influence of the Si-Si distance on the Si-Al adiabatic potential. The equilibrium distance between Si and Al atoms increases with the length of Si-Si bond increasing. It was concluded that description of Si clusters in Al matrix demands an account of the angle depending part of Si-Al interaction.

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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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Fraley, Chris, Adrian Raftery, and Ron Wehrensy. Incremental Model-Based Clustering for Large Datasets With Small Clusters. Defense Technical Information Center, 2003. http://dx.doi.org/10.21236/ada459790.

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Rojas, Thomas D. National forest economic clusters: a new model for assessing national-forest-based natural resources products and services. U.S. Department of Agriculture, Forest Service, Pacific Northwest Research Station, 2007. http://dx.doi.org/10.2737/pnw-gtr-703.

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Nagahi, Morteza, Raed Jaradat, Mohammad Nagahisarchoghaei, Ghodsieh Ghanbari, Sujan Poudyal, and Simon Goerger. Effect of individual differences in predicting engineering students' performance : a case of education for sustainable development. Engineer Research and Development Center (U.S.), 2021. http://dx.doi.org/10.21079/11681/40700.

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The academic performance of engineering students continues to receive attention in the literature. Despite that, there is a lack of studies in the literature investigating the simultaneous relationship between students' systems thinking (ST) skills, Five-Factor Model (FFM) personality traits, proactive personality scale, academic, demographic, family background factors, and their potential impact on academic performance. Three established instruments, namely, ST skills instrument with seven dimensions, FFM traits with five dimensions, and proactive personality with one dimension, along with a

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Bonita, Manuel, Fernando Correa, Harri Ahveninen, and Pertti Veijalainen. Forest Clusters: A Competitive Model for Latin America. Inter-American Development Bank, 2002. http://dx.doi.org/10.18235/0008811.

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contribute to success in the forest business. These include sound macroeconomic and long-term forest policies, secure land tenure, support from related education and technology programs, and cooperation among the various industries involved in producing and marketing forest products and services. The basic objective of this document is to examine the potential for creating forest clusters in Latin America. These clusters are based on the experience of Nordic countries and on studies of six natural resource-based clusters, identified by ECLAC/CEPAL. Specifically, this study aims to: 1) Identify

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Walizer, Laura, Robert Haehnel, Luke Allen, and Yonghu Wenren. Application of multi-fidelity methods to rotorcraft performance assessment. Engineer Research and Development Center (U.S.), 2024. http://dx.doi.org/10.21079/11681/48474.

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We present a Python-based multi-fidelity tool to estimate rotorcraft performance metrics. We use Gaussian-Process regression (GPR) methods to adaptively build a surrogate model using a small number of high-fidelity CFD points to improve estimates of performance metrics from a medium-fidelity comprehensive analysis model. To include GPR methods in our framework, we used the EmuKit Python package. Our framework adaptively chooses new high-fidelity points to run in regions where the model variance is high. These high-fidelity points are used to update the GPR model; convergence is reached when mo

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de Dieu Niyigena, Jean, Innocent Ngaruye, Joseph Nzabanita, and Martin Singull. Approximation of misclassification probabilities using quadratic classifier for repeated measurements with known covariance matrices. Linköping University Electronic Press, 2024. http://dx.doi.org/10.3384/lith-mat-r-2024-02.

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Quadratic discriminant analysis is a well-established supervised classification method, which extends the linear the linear discriminant analysis by relaxing the assumption of equal variances across classes. In this study, quadratic discriminant analysis is used to develop a quadratic classification rule based on repeated measurements. We employ a bilinear regression model to assign new observations to predefined populations and approximate the misclassification probability. Through weighted estimators, we estimate unknown mean parameters and derive moments of the quadratic classifier. We then

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Turner, Nigel E., Nicolas Trajtenberg, Steve Cook, Olga Sanchez de Ribera, Jing Shi, and Henrietta Bowden-Jones. A health inequality examination of problem gambling, substance abuse, mental health, and poverty in the United Kingdom; A secondary analysis and stakeholder interviews. Greo Evidence Insights, 2023. https://doi.org/10.33684/2024.003.

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Purpose: This project is focused on the social determinants of health associated with problem gambling and examined gambling-related disparities and the determinants of negative health outcomes. Social determinants include social class education, ethnic group, age, and sex (Elton-Marshall, et al., 2017). The main aims of the study were as follows: Aim 1: In this study, we used the large data set to determine subpopulations who are experiencing social inequity (e.g., youth, older adults, women, Black people and other minority ethnic groups, and people with low income; see Elton-Marshall, et al.

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