Готові списки джерел за темами / Module clustering

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Добірка наукової літератури з теми "Module clustering"

Автор: Grafiati

Опубліковано: 17 січня 2024

Оновлено: 19 липня 2025

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Статті в журналах з теми "Module clustering"

Alshareef, Haya, and Mashael Maashi. "Application of Multi-Objective Hyper-Heuristics to Solve the Multi-Objective Software Module Clustering Problem." Applied Sciences 12, no. 11 (2022): 5649. http://dx.doi.org/10.3390/app12115649.

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Software maintenance is an important step in the software lifecycle. Software module clustering is a HHMO_CF_GDA optimization problem involving several targets that require minimization of module coupling and maximization of software cohesion. Moreover, multi-objective software module clustering involves assembling a specific group of modules according to specific cluster criteria. Software module clustering classifies software modules into different clusters to enhance the software maintenance process. A structure with low coupling and high cohesion is considered an excellent software module

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Hou, Jie, Xiufen Ye, Chuanlong Li, and Yixing Wang. "K-Module Algorithm: An Additional Step to Improve the Clustering Results of WGCNA Co-Expression Networks." Genes 12, no. 1 (2021): 87. http://dx.doi.org/10.3390/genes12010087.

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Among biological networks, co-expression networks have been widely studied. One of the most commonly used pipelines for the construction of co-expression networks is weighted gene co-expression network analysis (WGCNA), which can identify highly co-expressed clusters of genes (modules). WGCNA identifies gene modules using hierarchical clustering. The major drawback of hierarchical clustering is that once two objects are clustered together, it cannot be reversed; thus, re-adjustment of the unbefitting decision is impossible. In this paper, we calculate the similarity matrix with the distance co

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Hu, Hai Yan, and You Qiao Zhang. "The Study and Realization of Energy-Aware Routing Algorithm of Wireless Sensor Networks." Applied Mechanics and Materials 201-202 (October 2012): 767–72. http://dx.doi.org/10.4028/www.scientific.net/amm.201-202.767.

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Based on the analysis of routing algorithm of typical wireless sensor networks, the author puts forward with the objectives of routing algorithm and designs energy-aware routing algorithm to reduce energy consumption and extend life cycle of the whole network. The algorithm constitutes four modules: clustering module, dynamic cluster head election module, dormant state module and inter-cluster routing module. Aiming at effectively using the energy of sensor nodes, the paper makes use of honeycomb-like two-level clustering structure to increase coverage rate of nodes. Also, studies of routing a

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Kirve, Shraddha. "Clustering Techniques in Wireless Sensor Networks: A Practical Study." International Journal for Research in Applied Science and Engineering Technology 9, no. VI (2021): 536–38. http://dx.doi.org/10.22214/ijraset.2021.34990.

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Our Solution for the Mentioned Problem Statement Comprised of Different Modules such as Alert &Notification Module, Real-Time Data Collection Module from Authenticated Source, Precaution Module to Define and Broadcast Protocol to Disaster Affected Areas, Social Media Message Circulation (SMMC) Module. IENS (Indian Early Notification System) has been designed by our team to Get & Fetch Notification System as soon as Disaster Stuck or Popped-Up (Introduce/Originated) and notifies as well as channelize Related Information via Different Social Media Official Platforms.

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Karayiannis, Dimitrios, and Spyros Tragoudas. "Clustering Network Modules with Different Implementations for Delay Minimization." VLSI Design 7, no. 1 (1998): 1–13. http://dx.doi.org/10.1155/1998/69289.

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In recent years there has been an extensive interest in clustering the modules of a network so that the maximum delay from any primary input to any primary output is minimized [8, 7, 6]. Clusters have a maximum capacity and modules may have different implementations. All existing CAD frameworks initially select an implementation of each module, and at a later stage they cluster the modules. We present an approach that clusters the nodes, while considering their alternative implementations, so that we further minimize the maximum delay after the clustering. Our approach is based on optimal algo

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Mohammad Shahid, Sunil Gupta, and MS. Sofia Pillai. "Machine Learning-Based False Positive Software Vulnerability Analysis." Global Journal of Innovation and Emerging Technology 1, no. 1 (2022): 29–35. http://dx.doi.org/10.58260/j.iet.2202.0105.

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Measurements and fault data from an older software version were used to build the fault prediction model for the new release. When past fault data isn't available, it's a problem. The software industry's assessment of programme module failure rates without fault labels is a difficult task. Unsupervised learning can be used to build a software fault prediction model when module defect labels are not available. These techniques can help identify programme modules that are more prone to errors. One method is to make use of clustering algorithms. Software module failures can be predicted using uns

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Strauch, Martin, Jochen Supper, Christian Spieth, et al. "A Two-Step Clustering for 3-D Gene Expression Data Reveals the Main Features of the Arabidopsis Stress Response." Journal of Integrative Bioinformatics 4, no. 1 (2007): 81–93. http://dx.doi.org/10.1515/jib-2007-54.

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Summary We developed an integrative approach for discovering gene modules, i.e. genes that are tightly correlated under several experimental conditions and applied it to a threedimensional Arabidopsis thaliana microarray dataset. The dataset consists of approximately 23000 genes responding to 9 abiotic stress conditions at 6-9 different points in time. Our approach aims at finding relatively small and dense modules lending themselves to a specific biological interpretation. In order to detect gene modules within this dataset, we employ a two-step clustering process. In the first step, a k-mean

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Yu, Limin, Xianjun Shen, Jincai Yang, Kaiping Wei, Duo Zhong, and Ruilong Xiang. "Hypergraph Clustering Based on Game-Theory for Mining Microbial High-Order Interaction Module." Evolutionary Bioinformatics 16 (January 2020): 117693432097057. http://dx.doi.org/10.1177/1176934320970572.

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Microbial community is ubiquitous in nature, which has a great impact on the living environment and human health. All these effects of microbial communities on the environment and their hosts are often referred to as the functions of these communities, which depend largely on the composition of the communities. The study of microbial higher-order module can help us understand the dynamic development and evolution process of microbial community and explore community function. Considering that traditional clustering methods depend on the number of clusters or the influence of data that does not

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Alam, M. K., Azrina Abd Aziz, S. A. Latif, and Azlan Awang. "Error-Aware Data Clustering for In-Network Data Reduction in Wireless Sensor Networks." Sensors 20, no. 4 (2020): 1011. http://dx.doi.org/10.3390/s20041011.

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A wireless sensor network (WSN) deploys hundreds or thousands of nodes that may introduce large-scale data over time. Dealing with such an amount of collected data is a real challenge for energy-constraint sensor nodes. Therefore, numerous research works have been carried out to design efficient data clustering techniques in WSNs to eliminate the amount of redundant data before transmitting them to the sink while preserving their fundamental properties. This paper develops a new error-aware data clustering (EDC) technique at the cluster-heads (CHs) for in-network data reduction. The proposed E

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Wu, Yong Liang, Bao Quan Mao, Li Xu, Dong Ming Dai, and Yan Chao Liu. "The Evaluation of Module Division Programme Based on Information Entropy." Advanced Materials Research 479-481 (February 2012): 1592–95. http://dx.doi.org/10.4028/www.scientific.net/amr.479-481.1592.

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Firstly, analysis product’s customer demand correlation, function correlation, geometric correlation, structure the corresponding correlation matrix, distribute the respective weighting factor, and then establish an integrated correlation matrix. Application of fuzzy clustering, the establishment of cluster map, the program has been divided into different modules. Based on information entropy theory, select product’s design and manufacturing complexity, cost, maintenance as the optimization objective, establish mathematical evaluation model of module division. Evaluating a number of options ge

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Дисертації з теми "Module clustering"

Ptitsyn, Andrey. "New algorithms for EST clustering." Thesis, University of the Western Cape, 2000. http://etd.uwc.ac.za/index.php?module=etd&amp.

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Expressed sequence tag database is a rich and fast growing source of data for gene expression analysis and drug discovery. Clustering of raw EST data is a necessary step for further analysis and one of the most challenging problems of modem computational biology.

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Passmoor, Sean Stuart. "Clustering studies of radio-selected galaxies." Thesis, University of the Western Cape, 2011. http://etd.uwc.ac.za/index.php?module=etd&action=viewtitle&id=gen8Srv25Nme4_7521_1332410859.

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We investigate the clustering of HI-selected galaxies in the ALFALFA survey and compare results with those obtained for HIPASS. Measurements of the angular correlation function and the inferred 3D-clustering are compared with results from direct spatial-correlation measurements. We are able to measure clustering on smaller angular scales and for galaxies with lower HI masses than was previously possible. We calculate the expected clustering of dark matter using the redshift distributions of HIPASS and ALFALFA and show that the ALFALFA sample is somewhat more anti-biased with respect to dark

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Javar, Shima. "Measurement and comparison of clustering algorithms." Thesis, Växjö University, School of Mathematics and Systems Engineering, 2007. http://urn.kb.se/resolve?urn=urn:nbn:se:vxu:diva-1735.

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In this project, a number of different clustering algorithms are described and their workings explained. They are compared to each other by implementing them on number of graphs with a known architecture.These clustering algorithm, in the order they are implemented, are as follows: Nearest neighbour hillclimbing, Nearest neighbour big step hillclimbing, Best neighbour hillclimbing, Best neighbour big step hillclimbing, Gem 3D, K-means simple, K-means Gem 3D, One cluster and One cluster per node.The graphs are Unconnected, Directed KX, Directed Cycle KX and Directed Cycle.<

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Hu, Yang. "PV Module Performance Under Real-world Test Conditions - A Data Analytics Approach." Case Western Reserve University School of Graduate Studies / OhioLINK, 2014. http://rave.ohiolink.edu/etdc/view?acc_num=case1396615109.

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Riedl, Pavel. "Modul shlukové analýzy systému pro dolování z dat." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2010. http://www.nusl.cz/ntk/nusl-237095.

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This master's thesis deals with development of a module for a data mining system, which is being developed on FIT. The first part describes the general knowledge discovery process and cluster analysis including cluster validation; it also describes Oracle Data Mining including algorithms, which it uses for clustering. At the end it deals with the system and the technologies it uses, such as NetBeans Platform and DMSL. The second part describes design of a clustering module and a module used to compare its results. It also deals with visualization of cluster analysis results and shows the achie

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Handfield, Louis-François. "Cis-regulatory modules clustering from sequence similarity." Thesis, McGill University, 2007. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=112632.

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I present a method that regroups cis-regulatory modules by shared sequences motifs. The goal of this approach is to search for clusters of modules that may share some function, using only sequence similarity. The proposed similarity measure is based on a variable-order Markov model likelihood scoring of sequences. I also introduce an extension of the variable-order Markov model which could better perform the required task. Results. I show that my method may recover subsets of sequences sharing a pattern in a set of generated sequences. I found that the proposed approach is successful in findin

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Wu, Jingwen. "Model-based clustering and model selection for binned data." Thesis, Supélec, 2014. http://www.theses.fr/2014SUPL0005/document.

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Cette thèse étudie les approches de classification automatique basées sur les modèles de mélange gaussiens et les critères de choix de modèles pour la classification automatique de données discrétisées. Quatorze algorithmes binned-EM et quatorze algorithmes bin-EM-CEM sont développés pour quatorze modèles de mélange gaussiens parcimonieux. Ces nouveaux algorithmes combinent les avantages des données discrétisées en termes de réduction du temps d’exécution et les avantages des modèles de mélange gaussiens parcimonieux en termes de simplification de l'estimation des paramètres. Les complexités d

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Sampson, Joshua Neil. "Clustering genes in genetical genomics /." Thesis, Connect to this title online; UW restricted, 2007. http://hdl.handle.net/1773/9549.

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Yelibi, Lionel. "Introduction to fast Super-Paramagnetic Clustering." Master's thesis, Faculty of Science, 2019. http://hdl.handle.net/11427/31332.

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We map stock market interactions to spin models to recover their hierarchical structure using a simulated annealing based Super-Paramagnetic Clustering (SPC) algorithm. This is directly compared to a modified implementation of a maximum likelihood approach to fast-Super-Paramagnetic Clustering (f-SPC). The methods are first applied standard toy test-case problems, and then to a dataset of 447 stocks traded on the New York Stock Exchange (NYSE) over 1249 days. The signal to noise ratio of stock market correlation matrices is briefly considered. Our result recover approximately clusters represen

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Mair, Patrick, and Marcus Hudec. "Session Clustering Using Mixtures of Proportional Hazards Models." Department of Statistics and Mathematics, WU Vienna University of Economics and Business, 2008. http://epub.wu.ac.at/598/1/document.pdf.

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Emanating from classical Weibull mixture models we propose a framework for clustering survival data with various proportionality restrictions imposed. By introducing mixtures of Weibull proportional hazards models on a multivariate data set a parametric cluster approach based on the EM-algorithm is carried out. The problem of non-response in the data is considered. The application example is a real life data set stemming from the analysis of a world-wide operating eCommerce application. Sessions are clustered due to the dwell times a user spends on certain page-areas. The solution allows for t

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Книги з теми "Module clustering"

J, Davis Cecil, Herman Irving P, and Turner Terry R, eds. Process module metrology, control, and clustering, 11-13 September 1991, San Jose, Calif. SPIE--International Society for Optical Engineering, 1991.

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J, Davis Cecil, Herman Irving P, Turner Terry R, and Society of Photo-Optical Instrumentation Engineers., eds. Process module metrology, control, and clustering: 11-13 September 1991, San Jose, California. SPIE, 1992.

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Sergiy, Butenko, Chaovalitwongse W. Art, Pardalos P. M. 1954-, and DIMACS Workshop on Clustering Problems in Biological Networks (2006 : Rutgers University), eds. Clustering challenges in biological networks. World Scientific, 2009.

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Wilson, Caroline L. Clustering algorithms and mathematical modeling. Nova Science Publishers, 2010.

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Rachev, S. T. Financial models with Lévy processes and volatility clustering. John Wiley, 2011.

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Tsangarides, Charalambos G. What is fuzzy about clustering in West Africa? International Monetary Fund, African Dept., 2006.

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Grigor'ev, Anatoliy, and Evgeniy Isaev. Methods and algorithms of data processing. INFRA-M Academic Publishing LLC., 2020. http://dx.doi.org/10.12737/1032305.

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The tutorial deals with selected methods and algorithms of data processing, the sequence of solving problems of processing and analysis of data to create models behavior of the object taking into account all the components of its mathematical model. Describes the types of technological methods for the use of software and hardware for solving problems in this area. The algorithms of distributions, regressions vremenny series, transform them with the aim of obtaining mathematical models and prediction of the behavior information and economic systems (objects). The second edition is supplem

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E, MacCuish Norah, ed. Clustering in bioinformatics and drug discovery. Taylor & Francis, 2011.

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Thurow, Amy Purvis. Toward an augmented theory of cooperative behavior: The case of clustering in animal agriculture. Dept. of Agricultural Economics, Texas A & M University, 1998.

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Частини книг з теми "Module clustering"

Horvath, Steve. "Clustering Procedures and Module Detection." In Weighted Network Analysis. Springer New York, 2011. http://dx.doi.org/10.1007/978-1-4419-8819-5_8.

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Paixao, Matheus, Mark Harman, and Yuanyuan Zhang. "Multi-objective Module Clustering for Kate." In Search-Based Software Engineering. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-22183-0_24.

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Yoshida, Ryo, Seiya Imoto, and Tomoyuki Higuchi. "A Penalized Likelihood Estimation on Transcriptional Module-Based Clustering." In Computational Science and Its Applications – ICCSA 2005. Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11424857_42.

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Lecca, Paola, and Angela Re. "Module Detection in Dynamic Networks by Temporal Edge Weight Clustering." In Computational Intelligence Methods for Bioinformatics and Biostatistics. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-44332-4_5.

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da Silva Júnior, Marcondes R., and Aluizio F. R. Araújo. "Subspace Clustering Multi-module Self-organizing Maps with Two-Stage Learning." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-15937-4_24.

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Liang, Dong, Jun Liu, Kuanquan Wang, Gongning Luo, Wei Wang, and Shuo Li. "Position-Prior Clustering-Based Self-attention Module for Knee Cartilage Segmentation." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-16443-9_19.

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de Oliveira Barros, Márcio. "Evaluating Modularization Quality as an Extra Objective in Multiobjective Software Module Clustering." In Search Based Software Engineering. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-23716-4_23.

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Ray, Sumanta, Sinchani Chakraborty, and Anirban Mukhopadhyay. "DCoSpect: A Novel Differentially Coexpressed Gene Module Detection Algorithm Using Spectral Clustering." In Advances in Intelligent Systems and Computing. Springer India, 2015. http://dx.doi.org/10.1007/978-81-322-2695-6_7.

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Zamli, Kamal Z., Fakhrud Din, Nazirah Ramli, and Bestoun S. Ahmed. "Software Module Clustering Based on the Fuzzy Adaptive Teaching Learning Based Optimization Algorithm." In Intelligent and Interactive Computing. Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-6031-2_3.

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Singh, Shipra, and Deepa Gupta. "Module Allocation Model in Distributed Computing System by Implementing Fuzzy C-means Clustering Technique." In Lecture Notes in Electrical Engineering. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-8136-4_14.

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Тези доповідей конференцій з теми "Module clustering"

Zhan, Dakui, and Khamron Sunat. "You Only Look Once with Clustering Segmentation Module." In 2025 5th International Conference on Advances in Electrical, Electronics and Computing Technology (EECT). IEEE, 2025. https://doi.org/10.1109/eect64505.2025.10966947.

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Chen, Wanghu, Qi Fan, Siqi Zeng, and Jing Li. "Clustering of Students’ Behavior Using the GPT-2 Module Based on the Macroscopic Attention Model." In 2024 IEEE International Conference on Big Data (BigData). IEEE, 2024. https://doi.org/10.1109/bigdata62323.2024.10825693.

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Seidel, Thomas E., and Michael R. Stark. "Learning opportunities through the use of cluster tools." In Process Module Metrology, Control and Clustering, edited by Cecil J. Davis, Irving P. Herman, and Terry R. Turner. SPIE, 1992. http://dx.doi.org/10.1117/12.56617.

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Lally, Kevin. "Equipment improvement methodology." In Process Module Metrology, Control and Clustering, edited by Cecil J. Davis, Irving P. Herman, and Terry R. Turner. SPIE, 1992. http://dx.doi.org/10.1117/12.56618.

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Seidel, J. P., W. Wachter, William M. Triggs, and Robert P. Hall. "Integrated deposition of TiN barrier layers in cluster tools." In Process Module Metrology, Control and Clustering, edited by Cecil J. Davis, Irving P. Herman, and Terry R. Turner. SPIE, 1992. http://dx.doi.org/10.1117/12.56619.

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Hauser, John R., and Syed A. Rizvi. "Cluster tool technology." In Process Module Metrology, Control and Clustering, edited by Cecil J. Davis, Irving P. Herman, and Terry R. Turner. SPIE, 1992. http://dx.doi.org/10.1117/12.56620.

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Wong, Fred, and George E. Zilberman. "Open architecture cluster tool: communication and user interface integration." In Process Module Metrology, Control and Clustering, edited by Cecil J. Davis, Irving P. Herman, and Terry R. Turner. SPIE, 1992. http://dx.doi.org/10.1117/12.56621.

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Boitnott, Charles A., and David R. Craven. "Single-wafer high-pressure oxidation." In Process Module Metrology, Control and Clustering, edited by Cecil J. Davis, Irving P. Herman, and Terry R. Turner. SPIE, 1992. http://dx.doi.org/10.1117/12.56622.

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Hansen, Brad. "Benefits of cluster tool architecture for implementation of evolutionary equipment improvements and applications." In Process Module Metrology, Control and Clustering, edited by Cecil J. Davis, Irving P. Herman, and Terry R. Turner. SPIE, 1992. http://dx.doi.org/10.1117/12.56623.

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Sitte, Renate, Sima Dimitrijev, and H. Barry Harrison. "Dynamic design processing of integrated circuits for an "on target" end product." In Process Module Metrology, Control and Clustering, edited by Cecil J. Davis, Irving P. Herman, and Terry R. Turner. SPIE, 1992. http://dx.doi.org/10.1117/12.56624.

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Звіти організацій з теми "Module clustering"

Oh, Man-Suk, and Adrian Raftery. Model-based Clustering with Dissimilarities: A Bayesian Approach. Defense Technical Information Center, 2003. http://dx.doi.org/10.21236/ada459759.

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Merl, D. Advances in Bayesian Model Based Clustering Using Particle Learning. Office of Scientific and Technical Information (OSTI), 2009. http://dx.doi.org/10.2172/1010386.

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De Leon, Phillip L., and Richard D. McClanahan. Efficient speaker verification using Gaussian mixture model component clustering. Office of Scientific and Technical Information (OSTI), 2012. http://dx.doi.org/10.2172/1039402.

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Bergman, O., and C. B. Thorn. Universality and clustering in 1 + 1 dimensional superstring-bit models. Office of Scientific and Technical Information (OSTI), 1996. http://dx.doi.org/10.2172/200667.

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Fraley, Chris, and Adrian E. Raftery. Bayesian Regularization for Normal Mixture Estimation and Model-Based Clustering. Defense Technical Information Center, 2005. http://dx.doi.org/10.21236/ada454825.

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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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Pulido-Mahecha, Karen L., Sergio Restrepo-Ángel, and Franky Juliano Galeano-Ramírez†. Measuring the Unmeasurable: Unraveling the complexities of real-time output gap estimation. Banco de la República, 2024. http://dx.doi.org/10.32468/be.1284.

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This paper evaluates seven output gap models for real-time estimates, based on three criteria: stability of estimations on new observations, data revisions and/or methodological changes; inflation forecasting accuracy; and potential output response to structural economic shocks. Results confirm no single model outperforms across all criteria. Structural VARs exhibit superior inflation forecasts but show high instability, while semi-structural models produce more theoretically consistent potential output responses. To overcome this trade-off, we propose a novel clustering approach to pool model

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Wehrens, Ron, Lutgarde M. Buydens, Chris Fraley, and Adrian E. Raftery. Model-Based Clustering for Image Segmentation and Large Datasets Via Sampling. Defense Technical Information Center, 2003. http://dx.doi.org/10.21236/ada459638.

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Murtagh, Fionn, Adrian E. Raftery, and Jean-Luc Starck. Bayesian Inference for Color Image Quantization via Model-Based Clustering Trees. Defense Technical Information Center, 2001. http://dx.doi.org/10.21236/ada459791.

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Fraley, Chris, and Adrian E. Raftery. MCLUST: Software for Model-Based Clustering, Density Estimation and Discriminant Analysis. Defense Technical Information Center, 2002. http://dx.doi.org/10.21236/ada459792.

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