Добірка наукової літератури з теми "Variables clustering"
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Статті в журналах з теми "Variables clustering"
Perricone, Chiara. "Clustering macroeconomic variables." Structural Change and Economic Dynamics 44 (March 2018): 23–33. http://dx.doi.org/10.1016/j.strueco.2018.02.001.
Повний текст джерелаHathaway, Richard J. "Clustering Random Variables." IETE Journal of Research 44, no. 4-5 (July 1998): 199–205. http://dx.doi.org/10.1080/03772063.1998.11416046.
Повний текст джерелаChen, Mingkun, and Evelyne Vigneau. "Supervised clustering of variables." Advances in Data Analysis and Classification 10, no. 1 (November 15, 2014): 85–101. http://dx.doi.org/10.1007/s11634-014-0191-5.
Повний текст джерелаZhang, Hongmei, Yubo Zou, Will Terry, Wilfried Karmaus, and Hasan Arshad. "Joint Clustering With Correlated Variables." American Statistician 73, no. 3 (July 9, 2018): 296–306. http://dx.doi.org/10.1080/00031305.2018.1424033.
Повний текст джерелаRubiano Moreno, Jesica, Carlos Alonso Malaver, Samuel Nucamendi Guillén, and Carlos López Hernández. "A clustering algorithm for ipsative variables." DYNA 86, no. 211 (October 1, 2019): 94–101. http://dx.doi.org/10.15446/dyna.v86n211.77835.
Повний текст джерелаForina, M., C. Armanino, and V. Raggio. "Clustering with dendrograms on interpretation variables." Analytica Chimica Acta 454, no. 1 (March 2002): 13–19. http://dx.doi.org/10.1016/s0003-2670(01)01517-3.
Повний текст джерелаSaracco, J., and M. Chavent. "Clustering of Variables for Mixed Data." EAS Publications Series 77 (2016): 121–69. http://dx.doi.org/10.1051/eas/1677007.
Повний текст джерелаHuh, Myung-Hoe, and Yong B. Lim. "Weighting variables in K-means clustering." Journal of Applied Statistics 36, no. 1 (October 31, 2008): 67–78. http://dx.doi.org/10.1080/02664760802382533.
Повний текст джерелаVigneau, E., and E. M. Qannari. "Clustering of Variables Around Latent Components." Communications in Statistics - Simulation and Computation 32, no. 4 (January 11, 2003): 1131–50. http://dx.doi.org/10.1081/sac-120023882.
Повний текст джерелаGhizlane, Ez-Zarrad, Sabbar Wafae, and Bekkhoucha Abdelkrim. "Features Clustering Around Latent Variables for High Dimensional Data." E3S Web of Conferences 297 (2021): 01070. http://dx.doi.org/10.1051/e3sconf/202129701070.
Повний текст джерелаДисертації з теми "Variables clustering"
Chang, Soong Uk. "Clustering with mixed variables /." [St. Lucia, Qld.], 2005. http://www.library.uq.edu.au/pdfserve.php?image=thesisabs/absthe19086.pdf.
Повний текст джерелаEndrizzi, Isabella <1975>. "Clustering of variables around latent components: an application in consumer science." Doctoral thesis, Alma Mater Studiorum - Università di Bologna, 2008. http://amsdottorato.unibo.it/667/1/Tesi_Endrizzi_Isabella.pdf.
Повний текст джерелаEndrizzi, Isabella <1975>. "Clustering of variables around latent components: an application in consumer science." Doctoral thesis, Alma Mater Studiorum - Università di Bologna, 2008. http://amsdottorato.unibo.it/667/.
Повний текст джерелаSaraiya, Devang. "The Impact of Environmental Variables in Efficiency Analysis: A fuzzy clustering-DEA Approach." Thesis, Virginia Tech, 2005. http://hdl.handle.net/10919/34637.
Повний текст джерелаMaster of Science
Dean, Nema. "Variable selection and other extensions of the mixture model clustering framework /." Thesis, Connect to this title online; UW restricted, 2006. http://hdl.handle.net/1773/8943.
Повний текст джерелаDoan, Nath-Quang. "Modèles hiérarchiques et topologiques pour le clustering et la visualisation des données." Paris 13, 2013. http://scbd-sto.univ-paris13.fr/secure/edgalilee_th_2013_doan.pdf.
Повний текст джерелаThis thesis focuses on clustering approaches inspired from topological models and an autonomous hierarchical clustering method. The clustering problem becomes more complicated and difficult due to the growth in quality and quantify of structured data such as graphs, trees or sequences. In this thesis, we are particularly interested in self-organizing maps which have been generally used for learning topological preservation, clustering, vector quantization and graph visualization. Our studyconcerns also a hierarchical clustering method AntTree which models the ability of real ants to build structure by connect themselves. By combining the topological map with the self-assembly rules inspired from AntTree, the goal is to represent data in a hierarchical and topological structure providing more insight data information. The advantage is to visualize the clustering results as multiple hierarchical trees and a topological network. In this report, we present three new models that are able to address clustering, visualization and feature selection problems. In the first model, our study shows the interest in the use of hierarchical and topological structure through several applications on numerical datasets, as well as structured datasets e. G. Graphs and biological dataset. The second model consists of a flexible and growing structure which does not impose the strict network-topology preservation rules. Using statistical characteristics provided by hierarchical trees, it accelerates significantly the learning process. The third model addresses particularly the issue of unsupervised feature selection. The idea is to use hierarchical structure provided by AntTree to discover automatically local data structure and local neighbors. By using the tree topology, we propose a new score for feature selection by constraining the Laplacian score. Finally, this thesis offers several perspectives for future work
Ndaoud, Mohamed. "Contributions to variable selection, clustering and statistical estimation inhigh dimension." Electronic Thesis or Diss., Université Paris-Saclay (ComUE), 2019. http://www.theses.fr/2019SACLG005.
Повний текст джерелаThis PhD thesis deals with the following statistical problems: Variable selection in high-Dimensional Linear Regression, Clustering in the Gaussian Mixture Model, Some effects of adaptivity under sparsity and Simulation of Gaussian processes.Under the sparsity assumption, variable selection corresponds to recovering the "small" set of significant variables. We study non-asymptotic properties of this problem in the high-dimensional linear regression. Moreover, we recover optimal necessary and sufficient conditions for variable selection in this model. We also study some effects of adaptation under sparsity. Namely, in the sparse vector model, we investigate, the changes in the estimation rates of some of the model parameters when the noise level or its nominal law are unknown.Clustering is a non-supervised machine learning task aiming to group observations that are close to each other in some sense. We study the problem of community detection in the Gaussian Mixture Model with two components, and characterize precisely the sharp separation between clusters in order to recover exactly the clusters. We also provide a fast polynomial time procedure achieving optimal recovery.Gaussian processes are extremely useful in practice, when it comes to model price fluctuations for instance. Nevertheless, their simulation is not easy in general. We propose and study a new rate-optimal series expansion to simulate a large class of Gaussian processes
Naik, Vaibhav C. "Fuzzy C-means clustering approach to design a warehouse layout." [Tampa, Fla.] : University of South Florida, 2004. http://purl.fcla.edu/fcla/etd/SFE0000437.
Повний текст джерелаNdaoud, Mohamed. "Contributions to variable selection, clustering and statistical estimation inhigh dimension." Thesis, Université Paris-Saclay (ComUE), 2019. http://www.theses.fr/2019SACLG005/document.
Повний текст джерелаThis PhD thesis deals with the following statistical problems: Variable selection in high-Dimensional Linear Regression, Clustering in the Gaussian Mixture Model, Some effects of adaptivity under sparsity and Simulation of Gaussian processes.Under the sparsity assumption, variable selection corresponds to recovering the "small" set of significant variables. We study non-asymptotic properties of this problem in the high-dimensional linear regression. Moreover, we recover optimal necessary and sufficient conditions for variable selection in this model. We also study some effects of adaptation under sparsity. Namely, in the sparse vector model, we investigate, the changes in the estimation rates of some of the model parameters when the noise level or its nominal law are unknown.Clustering is a non-supervised machine learning task aiming to group observations that are close to each other in some sense. We study the problem of community detection in the Gaussian Mixture Model with two components, and characterize precisely the sharp separation between clusters in order to recover exactly the clusters. We also provide a fast polynomial time procedure achieving optimal recovery.Gaussian processes are extremely useful in practice, when it comes to model price fluctuations for instance. Nevertheless, their simulation is not easy in general. We propose and study a new rate-optimal series expansion to simulate a large class of Gaussian processes
Giacofci, Joyce. "Classification non supervisée et sélection de variables dans les modèles mixtes fonctionnels. Applications à la biologie moléculaire." Thesis, Grenoble, 2013. http://www.theses.fr/2013GRENM025/document.
Повний текст джерелаMore and more scientific studies yield to the collection of a large amount of data that consist of sets of curves recorded on individuals. These data can be seen as an extension of longitudinal data in high dimension and are often modeled as functional data in a mixed-effects framework. In a first part we focus on performing unsupervised clustering of these curves in the presence of inter-individual variability. To this end, we develop a new procedure based on a wavelet representation of the model, for both fixed and random effects. Our approach follows two steps : a dimension reduction step, based on wavelet thresholding techniques, is first performed. Then a clustering step is applied on the selected coefficients. An EM-algorithm is used for maximum likelihood estimation of parameters. The properties of the overall procedure are validated by an extensive simulation study. We also illustrate our method on high throughput molecular data (omics data) like microarray CGH or mass spectrometry data. Our procedure is available through the R package "curvclust", available on the CRAN website. In a second part, we concentrate on estimation and dimension reduction issues in the mixed-effects functional framework. Two distinct approaches are developed according to these issues. The first approach deals with parameters estimation in a non parametrical setting. We demonstrate that the functional fixed effects estimator based on wavelet thresholding techniques achieves the expected rate of convergence toward the true function. The second approach is dedicated to the selection of both fixed and random effects. We propose a method based on a penalized likelihood criterion with SCAD penalties for the estimation and the selection of both fixed effects and random effects variances. In the context of variable selection we prove that the penalized estimators enjoy the oracle property when the signal size diverges with the sample size. A simulation study is carried out to assess the behaviour of the two proposed approaches
Книги з теми "Variables clustering"
Kessler, Ronald C. Trauma and PTSD in the United States. Edited by Charles B. Nemeroff and Charles R. Marmar. Oxford University Press, 2018. http://dx.doi.org/10.1093/med/9780190259440.003.0007.
Повний текст джерелаVariable Clustering Methods and Applications in Portfolio Selection. [New York, N.Y.?]: [publisher not identified], 2021.
Знайти повний текст джерелаBawa, Sandeep, Paul Wordsworth, and Inoshi Atukorala. Spondyloarthropathies. Oxford University Press, 2011. http://dx.doi.org/10.1093/med/9780199550647.003.010004.
Повний текст джерелаJames, Gareth. Sparseness and functional data analysis. Edited by Frédéric Ferraty and Yves Romain. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780199568444.013.11.
Повний текст джерелаЧастини книг з теми "Variables clustering"
Abdesselam, Rafik. "A Topological Clustering of Individuals." In Studies in Classification, Data Analysis, and Knowledge Organization, 1–9. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-09034-9_1.
Повний текст джерелаSteinley, Douglas. "Standardizing Variables in K-means Clustering." In Classification, Clustering, and Data Mining Applications, 53–60. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-642-17103-1_6.
Повний текст джерелаCantaluppi, Gabriele, and Marco Passarotti. "Clustering the Corpus of Seneca: A Lexical-Based Approach." In Advances in Latent Variables, 13–25. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/10104_2014_6.
Повний текст джерелаCouturier, Raphaël, Régis Gras, and Fabrice Guillet. "Reducing the Number of Variables Using Implicative Analysis." In Classification, Clustering, and Data Mining Applications, 277–85. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-642-17103-1_27.
Повний текст джерелаda Silva, Ana Lorga, Helena Bacelar-Nicolau, and Gilbert Saporta. "Missing Data in Hierarchical Classification of Variables — a Simulation Study." In Classification, Clustering, and Data Analysis, 121–28. Berlin, Heidelberg: Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/978-3-642-56181-8_13.
Повний текст джерелаDi Nuzzo, Cinzia, and Salvatore Ingrassia. "Three-Way Spectral Clustering." In Studies in Classification, Data Analysis, and Knowledge Organization, 111–19. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-09034-9_13.
Повний текст джерелаSilva, Ana Lorga, Gilbert Saporta, and Helena Bacelar-Nicolau. "Missing Data and Imputation Methods in Partition of Variables." In Classification, Clustering, and Data Mining Applications, 631–37. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-642-17103-1_59.
Повний текст джерелаMballo, Chérif, and Edwin Diday. "Kolmogorov-Smirnov for Decision Trees on Interval and Histogram Variables." In Classification, Clustering, and Data Mining Applications, 341–50. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-642-17103-1_33.
Повний текст джерелаAdjenughwure, Kingsley S., George N. Botzoris, and Basil K. Papadopoulos. "Clustering Variables Based on Fuzzy Equivalence Relations." In Advances in Intelligent Systems and Computing, 219–30. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-19704-3_18.
Повний текст джерелаHardy, André, and Pascale Lallemand. "Determination of the Number of Clusters for Symbolic Objects Described by Interval Variables." In Classification, Clustering, and Data Analysis, 311–18. Berlin, Heidelberg: Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/978-3-642-56181-8_34.
Повний текст джерелаТези доповідей конференцій з теми "Variables clustering"
Ha, Sungdo, and Emanuel Sachs. "Categories of process variables: robustness optimization, uniformity tuning, and mean adjustment." 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/10.1117/12.56636.
Повний текст джерелаGrinshpoun, Tal. "Clustering Variables by Their Agents." In 2015 IEEE / WIC / ACM International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT). IEEE, 2015. http://dx.doi.org/10.1109/wi-iat.2015.65.
Повний текст джерелаFerguson, Mark, Sam Devlin, Daniel Kudenko, and James Alfred Walker. "Player Style Clustering without Game Variables." In FDG '20: International Conference on the Foundations of Digital Games. New York, NY, USA: ACM, 2020. http://dx.doi.org/10.1145/3402942.3402960.
Повний текст джерелаYAN, Jian-Jun, Zhuo-Long WANG, Guo-Ping LIU, Zong-Jie HU, Yi-Qin WANG, and Rui GUO. "Establishment of Bayesian Networks with Latent Variables Based on Variable Clustering." In 2016 International Conference on Artificial Intelligence Science and Technology (AIST2016). WORLD SCIENTIFIC, 2017. http://dx.doi.org/10.1142/9789813206823_0070.
Повний текст джерелаSato-Ilic, Mika. "Weighted fuzzy clustering on subsets of variables." In 2007 9th International Symposium on Signal Processing and Its Applications (ISSPA). IEEE, 2007. http://dx.doi.org/10.1109/isspa.2007.4555525.
Повний текст джерелаRodriguez, Sara Ines Rizo, and Francisco de Assis Tenorio de Carvalho. "Clustering interval-valued data with automatic variables weighting." In 2019 International Joint Conference on Neural Networks (IJCNN). IEEE, 2019. http://dx.doi.org/10.1109/ijcnn.2019.8852220.
Повний текст джерелаHunyadi, Levente, and Istvan Vajk. "Identification of errors-in-variables systems using data clustering." In 2008 International Conference on Systems, Signals and Image Processing (IWSSIP). IEEE, 2008. http://dx.doi.org/10.1109/iwssip.2008.4604401.
Повний текст джерелаOh, C. H., H. Komatsu, K. Honda, and H. Ichihashi. "Fuzzy clustering algorithm extracting principal components independent of subsidiary variables." In Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks. IJCNN 2000. Neural Computing: New Challenges and Perspectives for the New Millennium. IEEE, 2000. http://dx.doi.org/10.1109/ijcnn.2000.861333.
Повний текст джерелаMinh, Nguyen Van, and Le Hoang Son. "Fuzzy Approaches to Context Variables in Fuzzy Geographically Weighted Clustering." In Second International Conference on Information Technology, Control, Chaos, Modeling and Applications. Academy & Industry Research Collaboration Center (AIRCC), 2015. http://dx.doi.org/10.5121/csit.2015.50503.
Повний текст джерелаTolner, Ferenc, Sandor Fegyverneki, Gyorgy Eigner, and Balazs Barta. "Clustering based on Preferences with K-modes using Categorical Variables." In 2021 IEEE 19th International Symposium on Intelligent Systems and Informatics (SISY). IEEE, 2021. http://dx.doi.org/10.1109/sisy52375.2021.9582485.
Повний текст джерелаЗвіти організацій з теми "Variables clustering"
Wang, Chih-Hao, and Na Chen. Do Multi-Use-Path Accessibility and Clustering Effect Play a Role in Residents' Choice of Walking and Cycling? Mineta Transportation Institute, June 2021. http://dx.doi.org/10.31979/mti.2021.2011.
Повний текст джерелаWeijters, Bert. Cluster Analysis in R: From Theory to Practice. Instats Inc., 2023. http://dx.doi.org/10.61700/3xjho79mx2fc0706.
Повний текст джерелаRaykov, Tenko. Latent Class Analysis and Mixture Modeling. Instats Inc., 2023. http://dx.doi.org/10.61700/tkd5fah8evykd469.
Повний текст джерела