Bibliografías temáticas / Variables clustering

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Literatura académica sobre el tema "Variables clustering"

Autor: Grafiati
Publicado: 9 de noviembre de 2024

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Artículos de revistas sobre el tema "Variables clustering"

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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.

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Hathaway, Richard J. "Clustering Random Variables." IETE Journal of Research 44, no. 4-5 (1998): 199–205. http://dx.doi.org/10.1080/03772063.1998.11416046.

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Chen, Mingkun, and Evelyne Vigneau. "Supervised clustering of variables." Advances in Data Analysis and Classification 10, no. 1 (2014): 85–101. http://dx.doi.org/10.1007/s11634-014-0191-5.

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Zhang, Hongmei, Yubo Zou, Will Terry, Wilfried Karmaus, and Hasan Arshad. "Joint Clustering With Correlated Variables." American Statistician 73, no. 3 (2018): 296–306. http://dx.doi.org/10.1080/00031305.2018.1424033.

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Rubiano Moreno, Jesica, Carlos Alonso Malaver, Samuel Nucamendi Guillén, and Carlos López Hernández. "A clustering algorithm for ipsative variables." DYNA 86, no. 211 (2019): 94–101. http://dx.doi.org/10.15446/dyna.v86n211.77835.

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The aim of this study is to introduce a new clustering method for ipsatives variables. This method can be used for nominals or ordinals variables for which responses must be mutually exclusive, and it is independent of data distribution. The proposed method is applied to outline motivational profiles for individuals based on a declared preferences set. A case study is used to analyze the performance of the proposed algorithm by comparing proposed method results versus the PAM method. Results show that proposed method generate a better segmentation and differentiated groups. An extensive study
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Forina, M., C. Armanino, and V. Raggio. "Clustering with dendrograms on interpretation variables." Analytica Chimica Acta 454, no. 1 (2002): 13–19. http://dx.doi.org/10.1016/s0003-2670(01)01517-3.

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Saracco, 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.

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Huh, Myung-Hoe, and Yong B. Lim. "Weighting variables in K-means clustering." Journal of Applied Statistics 36, no. 1 (2008): 67–78. http://dx.doi.org/10.1080/02664760802382533.

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Vigneau, E., and E. M. Qannari. "Clustering of Variables Around Latent Components." Communications in Statistics - Simulation and Computation 32, no. 4 (2003): 1131–50. http://dx.doi.org/10.1081/sac-120023882.

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Ghizlane, 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.

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Clustering of variables is the task of grouping similar variables into different groups. It may be useful in several situations such as dimensionality reduction, feature selection, and detect redundancies. In the present study, we combine two methods of features clustering the clustering of variables around latent variables (CLV) algorithm and the k-means based co-clustering algorithm (kCC). Indeed, classical CLV cannot be applied to high dimensional data because this approach becomes tedious when the number of features increases.
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Tesis sobre el tema "Variables clustering"

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Chang, Soong Uk. "Clustering with mixed variables /." [St. Lucia, Qld.], 2005. http://www.library.uq.edu.au/pdfserve.php?image=thesisabs/absthe19086.pdf.

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Endrizzi, Isabella <1975&gt. "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.

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The present work proposes a method based on CLV (Clustering around Latent Variables) for identifying groups of consumers in L-shape data. This kind of datastructure is very common in consumer studies where a panel of consumers is asked to assess the global liking of a certain number of products and then, preference scores are arranged in a two-way table Y. External information on both products (physicalchemical description or sensory attributes) and consumers (socio-demographic background, purchase behaviours or consumption habits) may be available in a row descriptor matrix X and in a
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Endrizzi, Isabella <1975&gt. "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/.

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The present work proposes a method based on CLV (Clustering around Latent Variables) for identifying groups of consumers in L-shape data. This kind of datastructure is very common in consumer studies where a panel of consumers is asked to assess the global liking of a certain number of products and then, preference scores are arranged in a two-way table Y. External information on both products (physicalchemical description or sensory attributes) and consumers (socio-demographic background, purchase behaviours or consumption habits) may be available in a row descriptor matrix X and in a
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Saraiya, Devang. "The Impact of Environmental Variables in Efficiency Analysis: A fuzzy clustering-DEA Approach." Thesis, Virginia Tech, 2005. http://hdl.handle.net/10919/34637.

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Data Envelopment Analysis (Charnes et al, 1978) is a technique used to evaluate the relative efficiency of any process or an organization. The efficiency evaluation is relative, which means it is compared with other processes or organizations. In real life situations different processes or units seldom operate in similar environments. Within a relative efficiency context, if units operating in different environments are compared, the units that operate in less desirable environments are at a disadvantage. In order to ensure that the comparison is fair within the DEA framework, a two-stage
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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.

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Doan, 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.

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Cette thèse se concentre sur les approches hiérarchiques et topologiques pour le clustering et la visualisation de données. Le problème du clustering devient de plus en plus compliqué en raison de présence de données structurées sous forme de graphes, arbres ou données séquentielles. Nous nous sommes particulièrement intéressés aux cartes auto-organisatrices et au modèle hiérarchique AntTree qui modélise la capacité des fourmis réelles. En combinant ces approches, l’objectif est de présenter les données dans une structure hiérarchique et topologique. Dans ce rapport, nous présentons trois modè
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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.

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Cette thèse traite les problèmes statistiques suivants : la sélection de variables dans le modèle de régression linéaire en grande dimension, le clustering dans le modèle de mélange Gaussien, quelques effets de l'adaptabilité sous l'hypothèse de parcimonie ainsi que la simulation des processus Gaussiens.Sous l'hypothèse de parcimonie, la sélection de variables correspond au recouvrement du "petit" ensemble de variables significatives. Nous étudions les propriétés non-asymptotiques de ce problème dans la régression linéaire en grande dimension. De plus, nous caractérisons les conditions optimal
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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.

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Ndaoud, Mohamed. "Contributions to variable selection, clustering and statistical estimation inhigh dimension." Thesis, Université Paris-Saclay (ComUE), 2019. http://www.theses.fr/2019SACLG005/document.

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Cette thèse traite les problèmes statistiques suivants : la sélection de variables dans le modèle de régression linéaire en grande dimension, le clustering dans le modèle de mélange Gaussien, quelques effets de l'adaptabilité sous l'hypothèse de parcimonie ainsi que la simulation des processus Gaussiens.Sous l'hypothèse de parcimonie, la sélection de variables correspond au recouvrement du "petit" ensemble de variables significatives. Nous étudions les propriétés non-asymptotiques de ce problème dans la régression linéaire en grande dimension. De plus, nous caractérisons les conditions optimal
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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.

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Un nombre croissant de domaines scientifiques collectent de grandes quantités de données comportant beaucoup de mesures répétées pour chaque individu. Ce type de données peut être vu comme une extension des données longitudinales en grande dimension. Le cadre naturel pour modéliser ce type de données est alors celui des modèles mixtes fonctionnels. Nous traitons, dans une première partie, de la classification non-supervisée dans les modèles mixtes fonctionnels. Nous présentons dans ce cadre une nouvelle procédure utilisant une décomposition en ondelettes des effets fixes et des effets aléatoir
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Libros sobre el tema "Variables clustering"

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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.

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Trauma and PTSD in the United States reviews epidemiological data on the prevalence and correlates of trauma and PTSD in the United States. The chapter begins by examining the comparative prevalence, age-of-onset distributions, and socio-demographic distributions of a wide range of specific traumatic life experiences. Data on the clustering and time-lagged associations among these different types of traumas are then considered. The chapter then reviews evidence on the absolute and relative risks of PTSD and the socio-demographic predictors of PTSD. Data are then reviewed on the course of PTSD
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Variable Clustering Methods and Applications in Portfolio Selection. [publisher not identified], 2021.

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Bawa, Sandeep, Paul Wordsworth, and Inoshi Atukorala. Spondyloarthropathies. Oxford University Press, 2011. http://dx.doi.org/10.1093/med/9780199550647.003.010004.

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♦ Spondyloarthropathies are related conditions typically associated with axial skeletal involvement, absence of rheumatoid factor, familial clustering, and a variable positive association with HLA-B27♦ Ankylosing spondylitis is the prototype with sacroiliac joint involvement being a prerequisite for diagnosis♦ Diagnosis is frequently delayed for several years but the use of magnetic resonance imaging to detect sacroiliitis greatly facilitates the establishment of an early diagnosis♦ Psoriatic arthritis, reactive arthritis, and enteropathic arthritis have prominent peripheral joint involvement
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James, 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.

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This article considers two functional data analysis settings where sparsity becomes important: the first involves only measurements at a relatively sparse set of points and the second relates to variable selection in a functional case. It begins with a discussion of two data sets that fall into the ‘sparsely observed’ category, the ‘growth’ data and the ‘nephropathy’ data, both of which are used to illustrate alternative approaches for analysing sparse functional data. It then examines different classes of methods that can be applied to functional data, such as basis functions, mixed-effects m
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Capítulos de libros sobre el tema "Variables clustering"

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Abdesselam, Rafik. "A Topological Clustering of Individuals." In Studies in Classification, Data Analysis, and Knowledge Organization. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-09034-9_1.

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AbstractThe clustering of objects-individuals is one of the most widely used approaches to exploring multidimensional data. The two common unsupervised clustering strategies are Hierarchical Ascending Clustering (HAC) and k-means partitioning used to identify groups of similar objects in a dataset to divide it into homogeneous groups. The proposed Topological Clustering of Individuals, or TCI, studies a homogeneous set of individual rows of a data table, based on the notion of neighborhood graphs; the columns-variables are more-or-less correlated or linked according to whether the variable is of a quantitative or qualitative type. It enables topological analysis of the clustering of individual variables which can be quantitative, qualitative or a mixture of the two. It first analyzes the correlations or associations observed between the variables in a topological context of principal component analysis (PCA) or multiple correspondence analysis (MCA), depending on the type of variable, then classifies individuals into homogeneous group, relative to the structure of the variables considered. The proposed TCI method is presented and illustrated here using a real dataset with quantitative variables, but it can also be applied with qualitative or mixed variables.
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Steinley, Douglas. "Standardizing Variables in K-means Clustering." In Classification, Clustering, and Data Mining Applications. Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-642-17103-1_6.

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Cantaluppi, Gabriele, and Marco Passarotti. "Clustering the Corpus of Seneca: A Lexical-Based Approach." In Advances in Latent Variables. Springer International Publishing, 2014. http://dx.doi.org/10.1007/10104_2014_6.

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Couturier, Raphaël, Régis Gras, and Fabrice Guillet. "Reducing the Number of Variables Using Implicative Analysis." In Classification, Clustering, and Data Mining Applications. Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-642-17103-1_27.

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da 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. Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/978-3-642-56181-8_13.

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Di Nuzzo, Cinzia, and Salvatore Ingrassia. "Three-Way Spectral 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_13.

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AbstractIn this paper, we present a spectral clustering approach for clustering three-way data. Three-way data concern data characterized by three modes: n units, p variables, and t different occasions. In other words, three-way data contain a t × p observed matrix for each statistical observation. The units generated by simultaneous observation of variables in different contexts are usually structured as three-way data, so each unit is basically represented as a matrix. In order to cluster the n units in K groups, the spectral clustering application to three-way data can be a powerful tool for unsupervised classification. Here, one example on real three-way data have been presented showing that spectral clustering method is a competitive method to cluster this type of data.
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Silva, Ana Lorga, Gilbert Saporta, and Helena Bacelar-Nicolau. "Missing Data and Imputation Methods in Partition of Variables." In Classification, Clustering, and Data Mining Applications. Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-642-17103-1_59.

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Mballo, Chérif, and Edwin Diday. "Kolmogorov-Smirnov for Decision Trees on Interval and Histogram Variables." In Classification, Clustering, and Data Mining Applications. Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-642-17103-1_33.

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Adjenughwure, Kingsley S., George N. Botzoris, and Basil K. Papadopoulos. "Clustering Variables Based on Fuzzy Equivalence Relations." In Advances in Intelligent Systems and Computing. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-19704-3_18.

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Hardy, André, and Pascale Lallemand. "Determination of the Number of Clusters for Symbolic Objects Described by Interval Variables." In Classification, Clustering, and Data Analysis. Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/978-3-642-56181-8_34.

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Actas de conferencias sobre el tema "Variables clustering"

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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.

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Grinshpoun, 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.

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Ferguson, 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. ACM, 2020. http://dx.doi.org/10.1145/3402942.3402960.

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YAN, 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.

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Sato-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.

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Rodriguez, 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.

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Hunyadi, 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.

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Oh, 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.

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Minh, 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.

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Tolner, 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.

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Informes sobre el tema "Variables clustering"

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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, 2021. http://dx.doi.org/10.31979/mti.2021.2011.

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The transportation studies literature recognizes the relationship between accessibility and active travel. However, there is limited research on the specific impact of walking and cycling accessibility to multi-use paths on active travel behavior. Combined with the culture of automobile dependency in the US, this knowledge gap has been making it difficult for policy-makers to encourage walking and cycling mode choices, highlighting the need to promote a walking and cycling culture in cities. In this case, a clustering effect (“you bike, I bike”) can be used as leverage to initiate such a trend
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Weijters, Bert. Cluster Analysis in R: From Theory to Practice. Instats Inc., 2023. http://dx.doi.org/10.61700/3xjho79mx2fc0706.

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This Cluster Analysis in R workshop, led by professor Bert Weijters from Ghent University, provides participants with a comprehensive understanding of the theory and practice of cluster analysis, a crucial tool in academic research for identifying patterns within datasets, including datasets with large numbers of cases and/or variables. This hands-on workshop covers topics from a very brief introduction to RStudio and cluster analysis, to mastering different clustering techniques, and provides practical exercises on simulated and real-world datasets, equipping participants with valuable skills
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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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