Relevant bibliographies by topics / Variables clustering

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Variables clustering'

Author: Grafiati
Published: 9 November 2024

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Journal articles on the topic "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 (July 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 (November 15, 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 (July 9, 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 (October 1, 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 was conducted to validate the performance clustering method against a set of random groups by clustering measures.
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Forina, 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.

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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 (October 31, 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 (January 11, 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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Dissertations / Theses on the topic "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 column descriptor matrix Z respectively. The aim of this method is to automatically provide a consumer segmentation where all the three matrices play an active role in the classification, getting homogeneous groups from all points of view: preference, products and consumer characteristics. The proposed clustering method is illustrated on data from preference studies on food products: juices based on berry fruits and traditional cheeses from Trentino. The hedonic ratings given by the consumer panel on the products under study were explained with respect to the product chemical compounds, sensory evaluation and consumer socio-demographic information, purchase behaviour and consumption habits.
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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 column descriptor matrix Z respectively. The aim of this method is to automatically provide a consumer segmentation where all the three matrices play an active role in the classification, getting homogeneous groups from all points of view: preference, products and consumer characteristics. The proposed clustering method is illustrated on data from preference studies on food products: juices based on berry fruits and traditional cheeses from Trentino. The hedonic ratings given by the consumer panel on the products under study were explained with respect to the product chemical compounds, sensory evaluation and consumer socio-demographic information, purchase behaviour and consumption habits.
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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 framework is presented in this thesis. Fuzzy clustering is used in the first stage to suitably group the units with similar environments. In a subsequent stage, a relative efficiency analysis is performed on these groups. By approaching the problem in this manner the influence of environmental variables on the efficiency analysis is removed. The concept of environmental dependency index is introduced in this thesis. The EDI reflects the extent to which the efficiency behavior of units is due to their environment of operation. The EDI also assists the decision maker to choose appropriate peers to guide the changes that the inefficient units need to make. A more rigorous series of steps to obtain the clustering solution is also presented in a separate chapter (chapter 5).
Master of Science
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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 modèles, dans le premier modèle nous montrons l’intérêt d’utiliser les structures hiérarchiques et topologiques sur des ensembles de données structurés sous forme de graphes. Le second modèle est une version incrémentale qui n’impose pas de règles sur la préservation de la topologie. Le troisième modèle aborde notamment la problématique de la sélection de variable en utilisant la structure hiérarchique, nous proposons un nouveau score pour sélectionner les variables pertinentes en contraignant le score Laplacien. Enfin, cette thèse propose plusieurs perspectives pour des travaux futurs
This 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
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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 optimales nécessaires et suffisantes pour la sélection de variables dans ce modèle. Nous étudions également certains effets de l'adaptation sous la même hypothèse. Dans le modèle à vecteur parcimonieux, nous analysons les changements dans les taux d'estimation de certains des paramètres du modèle lorsque le niveau de bruit ou sa loi nominale sont inconnus.Le clustering est une tâche d'apprentissage statistique non supervisée visant à regrouper des observations proches les unes des autres dans un certain sens. Nous étudions le problème de la détection de communautés dans le modèle de mélange Gaussien à deux composantes, et caractérisons précisément la séparation optimale entre les groupes afin de les recouvrir de façon exacte. Nous fournissons également une procédure en temps polynomial permettant un recouvrement optimal des communautés.Les processus Gaussiens sont extrêmement utiles dans la pratique, par exemple lorsqu'il s'agit de modéliser les fluctuations de prix. Néanmoins, leur simulation n'est pas facile en général. Nous proposons et étudions un nouveau développement en série à taux optimal pour simuler une grande classe de processus Gaussiens
This 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
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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 optimales nécessaires et suffisantes pour la sélection de variables dans ce modèle. Nous étudions également certains effets de l'adaptation sous la même hypothèse. Dans le modèle à vecteur parcimonieux, nous analysons les changements dans les taux d'estimation de certains des paramètres du modèle lorsque le niveau de bruit ou sa loi nominale sont inconnus.Le clustering est une tâche d'apprentissage statistique non supervisée visant à regrouper des observations proches les unes des autres dans un certain sens. Nous étudions le problème de la détection de communautés dans le modèle de mélange Gaussien à deux composantes, et caractérisons précisément la séparation optimale entre les groupes afin de les recouvrir de façon exacte. Nous fournissons également une procédure en temps polynomial permettant un recouvrement optimal des communautés.Les processus Gaussiens sont extrêmement utiles dans la pratique, par exemple lorsqu'il s'agit de modéliser les fluctuations de prix. Néanmoins, leur simulation n'est pas facile en général. Nous proposons et étudions un nouveau développement en série à taux optimal pour simuler une grande classe de processus Gaussiens
This 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
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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 aléatoires. Notre approche se décompose en deux étapes : une étape de réduction de dimension basée sur les techniques de seuillage des ondelettes et une étape de classification où l'algorithme EM est utilisé pour l'estimation des paramètres par maximum de vraisemblance. Nous présentons des résultats de simulations et nous illustrons notre méthode sur des jeux de données issus de la biologie moléculaire (données omiques). Cette procédure est implémentée dans le package R "curvclust" disponible sur le site du CRAN. Dans une deuxième partie, nous nous intéressons aux questions d'estimation et de réduction de dimension au sein des modèles mixtes fonctionnels et nous développons en ce sens deux approches. La première approche se place dans un objectif d'estimation dans un contexte non-paramétrique et nous montrons dans ce cadre, que l'estimateur de l'effet fixe fonctionnel basé sur les techniques de seuillage par ondelettes possède de bonnes propriétés de convergence. Notre deuxième approche s'intéresse à la problématique de sélection des effets fixes et aléatoires et nous proposons une procédure basée sur les techniques de sélection de variables par maximum de vraisemblance pénalisée et utilisant deux pénalités SCAD sur les effets fixes et les variances des effets aléatoires. Nous montrons dans ce cadre que le critère considéré conduit à des estimateurs possédant des propriétés oraculaires dans un cadre où le nombre d'individus et la taille des signaux divergent. Une étude de simulation visant à appréhender les comportements des deux approches développées est réalisée dans ce contexte
More 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
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Books on the topic "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 and the associations of trauma type and socio-demographic variables with the course of PTSD.
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Variable Clustering Methods and Applications in Portfolio Selection. [New York, N.Y.?]: [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 with variable degrees of spinal involvement♦ Non-steroidal anti-inflammatory drugs and physical therapy are the cornerstones of management but slow-acting disease-modifying antirheumatic drugs only have a role in peripheral arthritis♦ Anti-tumour necrosis factor biologic agents have revolutionized the treatment of the spondyloarthropathies.
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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 models and local smoothing techniques, as well as specific methodologies for dealing with sparse functional data in the principal components, clustering, classification, and regression settings. Finally, it describes two approaches for performing regressions involving a functional predictor and a scalar response: SASDA (sequential algorithm for selecting design automatically) and FLiRTI (Functional Linear Regression That’s Interpretable).
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Book chapters on the topic "Variables clustering"

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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.

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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, 53–60. Berlin, Heidelberg: 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, 13–25. Cham: 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, 277–85. Berlin, Heidelberg: 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, 121–28. Berlin, Heidelberg: 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, 111–19. Cham: 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, 631–37. Berlin, Heidelberg: 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, 341–50. Berlin, Heidelberg: 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, 219–30. Cham: 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, 311–18. Berlin, Heidelberg: Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/978-3-642-56181-8_34.

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Conference papers on the topic "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. New York, NY, USA: 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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Reports on the topic "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, June 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. This project contributes to the literature as one of the few published research projects that considers all typical categories of explanatory variables (individual and household socioeconomics, local built environment features, and travel and residential choice attitudes) as well as two new variables (accessibility to multi-use paths calculated by ArcGIS and a clustering effect represented by spatial autocorrelation) at two levels (level 1: binary choice of cycling/waking; level 2: cycling/walking time if yes at level 1) to better understand active travel demand. We use data from the 2012 Utah Travel Survey. At the first level, we use a spatial probit model to identify whether and why Salt Lake City residents walked or cycled. The second level is the development of a spatial autoregressive model for walkers and cyclists to examine what factors affect their travel time when using walking or cycling modes. The results from both levels, obtained while controlling for individual, attitudinal, and built-environment variables, show that accessibility to multi-use paths and a clustering effect (spatial autocorrelation) influence active travel behavior in different ways. Specifically, a cyclist is likely to cycle more when seeing more cyclists around. These findings provide analytical evidence to decision-makers for efficiently evaluating and deciding between plans and policies to enhance active transportation based on the two modeling approaches to assessing travel behavior described above.
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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 applicable in their own research.
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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 and inference are used throughout in the popular program Mplus. An official Instats certificate of completion is provided at the conclusion of the seminar. For European PhD students, the seminar offers 2 ECTS Equivalent points.
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