Tesis sobre el tema "Segmentation multivariée"
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Asseraf, Mounir. "Extension et optimisation pour la segmentation de la distance de Kolmogorov-Smirnov". Paris 9, 1998. https://portail.bu.dauphine.fr/fileviewer/index.php?doc=1998PA090026.
Ghandi, Sanaa. "Analysis of network delay measurements : Data mining methods for completion and segmentation". Electronic Thesis or Diss., Ecole nationale supérieure Mines-Télécom Atlantique Bretagne Pays de la Loire, 2023. http://www.theses.fr/2023IMTA0382.
The exponential growth of the Internet requires regular monitoring of network metrics. This thesis focuses on round-trip delays and the possibility of addressing the problems of missing data and multivariate segmentation. The first contribution includes the orchestration of delay measurement campaigns, as well as the development of a simulator that generates end-to-end delay traces. The second contribution of this thesis is the introduction of two missing data completion methods. The first is based on non-negative matrix factorization, while the second uses collaborative neural filtering. Tested on synthetic and real data, these methods demonstrate their efficiency and accuracy. The third contribution of this thesis involves multivariate delay segmentation. This approach is based on hierarchical clustering and is implemented in two stages. Firstly, the delay time series are grouped to obtain, within the same group, series with similar and synchronous variations and trends. Next, the multivariate segmentation step collectively and jointly segments the series within each group. This step uses hierarchical clustering followed by post-processing using the Viterbi algorithm to smooth the segmentation result. This method was tested on real delay traces from two major events affecting two Internet Exchange Points (IXPs). The results show that this method approximates the state-of-the-art in segmentation, while significantly reducing computing speed and costs
Rzadca, Mark C. "Multivariate granulometry and its application to texture segmentation /". Online version of thesis, 1994. http://hdl.handle.net/1850/12200.
Templeton, William James. "Consumer interests as market segmentation variables". Thesis, London Business School (University of London), 1986. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.312926.
Rye, Morten Beck. "Image segmentation and multivariate analysis in two-dimensional gel electrophoresis". Doctoral thesis, Norwegian University of Science and Technology, Department of Chemistry, 2007. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-1744.
The topic of this thesis is data-analysis on images from two-dimensional electrophoretic gels. Because of the complexity of these images, there are numerous steps and approaches to such an analysis, and no “golden standard” has yet been established on how to produce the desired output. In this thesis focus is put on two essential fields concerning 2D-gel analysis; registration of images by segregation and protein spot identification, and data-analysis on the output of such a registration by multivariate methods. Image segmentation is mainly concerned with the task of identifying individual protein spots in a gel-image. This has generally been the natural starting point of all methods and procedures developed since the introduction of 2D-gels in the mid-seventies, simply because this best reproduces the results created by a human analyst, who manually identify protein-spot entities. The amount of data produced in a 2D-gel experiment can be quite large, especially in multiple gels where the human analyst is dependent on additional statistical data-analytical tools to produce results. Because of the correlated nature of most gel-data, analysis by multivariate methods is natural choice, and are therefore adopted in this thesis. The goal of this thesis is to introduce the above mentioned procedures at different stages in the analysis pipeline where they are not yet fully exploited, rather than to improve already existing algorithms. In this way new insight and ideas on how to handle data from 2D-gel experiments are achieved. The thesis starts with a review of segmentation methodology, and introduces a selected procedure used to identify protein spots throughout. Output from the segmentation is then used to create a multivariate spot-filtering model, which aims to separate protein spots from noise and artefacts often creating problems in 2D-gel analysis. Lately the use of common spot boundaries in multiple gels have been the method of choice when gels are analysed. How such boundaries should be defined is an important subject of discussion, and thus a new method for defining common boundaries based on the individual segmentation of each gel is introduced. Segmentation may be a natural starting point when gels are analysed, but it is not necessarily the most correct. Often the introduction of fixed spot entities introduces restrictions to the data which cause problems at later stages in the analysis. Analysing pixels from multiple gels directly has no such restrictions, and it is shown in this thesis that the output of such an analysis based on multivariate methods can produce very useful results. It can also give insight to the data problematic to achieve with the spot boundary approach. At last in the thesis an improved pixel-based approach is introduced, where a less restricted segmentation is used to reduce and concentrate the amount of data analysed, improving the final output.
Lu, Jiang. "Transforms for multivariate classification and application in tissue image segmentation /". free to MU campus, to others for purchase, 2002. http://wwwlib.umi.com/cr/mo/fullcit?p3052195.
Hosseini-Chaleshtari, Jamshid. "Segment Congruence Analysis: An Information Theoretic Approach". PDXScholar, 1987. https://pdxscholar.library.pdx.edu/open_access_etds/797.
On, Vu Ngoc Minh. "A new minimum barrier distance for multivariate images with applications to salient object detection, shortest path finding, and segmentation". Electronic Thesis or Diss., Sorbonne université, 2020. http://www.theses.fr/2020SORUS454.
Hierarchical image representations are widely used in image processing to model the content of an image in the multi-scale structure. A well-known hierarchical representation is the tree of shapes (ToS) which encodes the inclusion relationship between connected components from different thresholded levels. This kind of tree is self-dual, contrast-change invariant and popular in computer vision community. Typically, in our work, we use this representation to compute the new distance which belongs to the mathematical morphology domain. Distance transforms and the saliency maps they induce are generally used in image processing, computer vision, and pattern recognition. One of the most commonly used distance transforms is the geodesic one. Unfortunately, this distance does not always achieve satisfying results on noisy or blurred images. Recently, a new pseudo-distance, called the minimum barrier distance (MBD), more robust to pixel fluctuation, has been introduced. Some years after, Géraud et al. have proposed a good and fast-to-compute approximation of this distance: the Dahu pseudodistance. Since this distance was initially developed for grayscale images, we propose here an extension of this transform to multivariate images; we call it vectorial Dahu pseudo-distance. This new distance is easily and efficiently computed thanks to the multivariate tree of shapes (MToS). We propose an efficient way to compute this distance and its deduced saliency map in this thesis. We also investigate the properties of this distance in dealing with noise and blur in the image. This distance has been proved to be robust for pixel invariant. To validate this new distance, we provide benchmarks demonstrating how the vectorial Dahu pseudo-distance is more robust and competitive compared to other MB-based distances. This distance is promising for salient object detection, shortest path finding, and object segmentation. Moreover, we apply this distance to detect the document in videos. Our method is a region-based approach which relies on visual saliency deduced from the Dahu pseudo-distance. We show that the performance of our method is competitive with state-of-the-art methods on the ICDAR Smartdoc 2015 Competition dataset
Liggett, Rachel Esther. "Multivariate Approaches for Relating Consumer Preference to Sensory Characteristics". The Ohio State University, 2010. http://rave.ohiolink.edu/etdc/view?acc_num=osu1282868174.
Johansson, David. "Automatic Device Segmentation for Conversion Optimization : A Forecasting Approach to Device Clustering Based on Multivariate Time Series Data from the Food and Beverage Industry". Thesis, Luleå tekniska universitet, Institutionen för system- och rymdteknik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-81476.
Motta, Sergio Luis Stirbolov. "Estudo sobre segmentação de mercado consumidor por atitude e atributos ecológicos de produtos". Universidade de São Paulo, 2009. http://www.teses.usp.br/teses/disponiveis/12/12139/tde-30062009-161308/.
This study intended to verify if the variable attitude in conjunction with the consumer good´s ecologically characteristics may be used as market segmentation´s basis. To satisfy this proposition, we tried, at first, to know all the available theory about the topics that are related to and also the basis to the field research. It was a quantitative and descriptive one, with a field study method. A non-probabilistic sample of students and teachers was used to explain their opinions by self-administration of a strucured and disguised questionnaire. The data analysis ocurred by the application of three multivariate techniques: Factor Analysis, Cluster Analysis and Correspondence Analysis. The first of them was successfull, whereas it was possible to reduce the set of variables to two factors; the fatorial scores performed as inputs to the Cluster Analysis. This technique was successful too, because the majority of simulations combining similarity measures and aglomeration methods engendered clusters, which permitted an answer favorable to the research problem; one of the combinations Euclidean Square Distance and Withinn Groups was considered the most satisfactory and used as basis to the next technique, the Correspondence Analysis. It was applied to profile the clusters and give a relevance to this paper; it was partly successful, as we couldnt use some variables and it was replaced by Cross Tabulation. The final considerations confirmed the researchers expectation as regard to the possibility of obtain clusters using at the same time the variable attitude and good´s ecologically characteristics.
Lung-Yut-Fong, Alexandre. "Détection de ruptures pour les signaux multidimensionnels. Application à la détection d'anomalies dans les réseaux". Phd thesis, Télécom ParisTech, 2011. http://pastel.archives-ouvertes.fr/pastel-00675543.
Rosa, Marlise. "SEGMENTAÇÃO DE GRÃOS DE HEMATITA EM AMOSTRAS DE MINÉRIO DE FERRO POR ANÁLISE DE IMAGENS DE LUZ POLARIZADA". Universidade Federal de Santa Maria, 2008. http://repositorio.ufsm.br/handle/1/8064.
The aim of the present work is to classify co-registered pixels of stacks of polarized light images of iron ore into their respective crystalline grains or pores, thus producing grain segmented images that can be analyzed by their size, shape and orientation distributions, as well as their porosity and the size and morphology of the pores. Polished sections of samples of hematite-rich ore are digitally imaged in a rotating polarizer microscope at varying planepolarization angles. An image stack is produced for every field of view, where each image corresponds to a polarizer position. Any point in the sample is registered to the same pixel coordinates at all images in the stack. The resulting set of intensities for each pixel is directly related to the orientation of the crystal sampled at the corresponding position. Multivariate analysis of the sets of intensities leads to the classification of the pixels into their respective crystalline grains. Individual hematite grains of iron ore, as well as their pores, are segmented. The results are compared to those obtained by visual point counting methods.
O objetivo do presente trabalho é classificar pixels co-registrados de pilhas de imagens de luz polarizada de minério de ferro nos seus respectivos grãos cristalinos ou poros, produzindo assim imagens segmentadas por grãos que podem ser analisados quanto às suas distribuições de tamanho, forma e orientação, bem como sua porosidade, tamanho e forma dos poros. Seções polidas de amostras de minério de ferro rico em hematita foram imageadas difratalmente em um microscópio com polarizador giratório em ângulos variados de polarização. Uma pilha de imagens foi produzida para cada campo na qual cada imagem corresponde a uma orientação do polarizador. Cada ponto na amostra foi registrado nas mesmas coordenadas em todas as imagens da pilha. O conjunto resultante de intensidades de cada pixel está diretamente relacionado com a orientação do cristal amostrado na posição correspondente. A análise multivariada dos conjuntos de intensidades leva à classificação dos pixels nos seus respectivos grãos cristalinos. Grãos individuais de hematita do minério de ferro, bem como os seus poros foram segmentados. Os resultados foram comparados com aqueles obtidos pelo método de contagem dos pontos, ou seja, por inspeção visual.
Costa, Wilian França. "Segmentação multiresolução variográfica ótima". Universidade de São Paulo, 2016. http://www.teses.usp.br/teses/disponiveis/3/3141/tde-15122016-082121/.
Information extraction of data derived from remote sensing and other geotechnologies is important for many activities, e.g., the identification of environmental requirements, the definition of conservation areas, the planning and implementation of activities regarding compliance of correct land use; the management of natural resources, the definition of protected ecosystem areas, and the spatial planning of agricultural input reposition. This thesis presents a parameter optimisation method for the Multiresolution segmentation algorithm. The goal of the method is to obtain maximum sized segments within the established heterogeneity limits. The method makes use of variography, a geostatistical tool that gives a measure of how much two samples will vary in a region depending on the distance between each one of them. The variogram nugget effect is measured for each attribute layer and then averaged to obtain the optimal value for spatial segmentation with the Multiresolution algorithm. The segments thus obtained are superimposed on a regularly spaced sampled grid of georeferenced data to divide the region under study. To show the usefulnesss of this method, the following three case studies were performed: (i) the delineation of precision farming management zones; (ii) the selection of regions for environmental degradation estimates in the neighbourhood of species occurrence points; and (iii) the identification of bioclimatic regions that are present in biodiversity conservation units.
Barrera, Núñez Víctor Augusto. "Automatic diagnosis of voltage disturbances in power distribution networks". Doctoral thesis, Universitat de Girona, 2012. http://hdl.handle.net/10803/80944.
En esta tesis se propone una metodología para la identificación de la localización relativa (aguas arriba/abajo) y la causa de una perturbación eléctrica. La metodología utiliza las ondas trifásicas de tensión y de corriente registradas en redes de distribución radial sin presencia de generación distribuida. La metodología es validada utilizando perturbaciones eléctricas reales y simuladas. La metodología involucra atributos que han sido concebidos basándose en principios eléctricos e hipótesis de acuerdo a cada uno de los fenómenos eléctricos analizados. Se propusieron atributos tanto basados en la forma de onda como en la fecha de ocurrencia de la perturbación. La cantidad de información contenida y/o explicada por cada atributo es valorada mediante la aplicación del análisis multivariante de la varianza y algoritmos de extracción automática de reglas de decisión. Los resultados de clasificación muestran que la metodología propuesta puede ser utilizada para el diagnóstico automático de perturbaciones eléctricas registradas en redes de distribución radial. Los resultados de diagnóstico pueden ser utilizados para apoyar las tareas de operación, mantenimiento y planeamiento de las redes de distribución.
Saker, Halima. "Segmentation of Heterogeneous Multivariate Genome Annotation Data". 2021. https://ul.qucosa.de/id/qucosa%3A75914.
Ungerer, Leona M. "Values as multivariate consumer market segmentation discriminators : a subjective well-being approach". Thesis, 2009. http://hdl.handle.net/10500/3188.
Psychology
D. Litt. et Phil. (Psychology))
Amornnikun, Patipharn y Patipharn Amornnikun. "Metaheuristic-Based Possibilistic Multivariate Fuzzy Weighted C-Means Algorithms for Market Segmentation". Thesis, 2019. http://ndltd.ncl.edu.tw/handle/swmwcm.
Hsiu-Wen, Liu. "Hierarchical Bayes Conjoint Analysis with Multivariate Mixture of Normal Heterogeneity: Implications for Market Segmentation". 2007. http://www.cetd.com.tw/ec/thesisdetail.aspx?etdun=U0001-2304200709150500.
Liu, Hsiu-Wen y 劉秀雯. "Hierarchical Bayes Conjoint Analysis with Multivariate Mixture of Normal Heterogeneity: Implications for Market Segmentation". Thesis, 2007. http://ndltd.ncl.edu.tw/handle/47042305067659434744.
國立臺灣大學
國際企業學研究所
95
Conjoint analysis, designed to estimate individual preference and the relative competition among brands, has become one of the most widely-used quantitative methods in Marketing Research. In addition, hierarchical Bayes inference is one of the most favored approaches because of its superior in recovering individual part-worths. However, current application of hierarchical Bayes model is not without drawbacks, because consumer heterogeneity is assumed to follow a multivariate normal distribution. The normal distribution has its own characteristic such as unimodal, symmetric and inverted U shape, which might lead to bias or limitation in part-worth density inference. Alternatively, the mixture of normal distributions is a more flexible and general approach in modeling consumer heterogeneity. It is especially suitable for the heterogeneity density inference, such as the unknown consumer heterogeneity distribution. It is flexible in modeling any symmetric or asymmetric distributions with either multi-modes or heavy tails. Furthermore, the normal distribution is just a special case of mixture of normal distributions. In this study, we develop a Bayesian Inference of multivariate mixture of normal distributions. Then, the model is applied in different hierarchical Bayes models as an assumption to modeling consumer heterogeneity. Two approaches in recent hierarchical Bayes conjoint analysis will be studied. They are continuous response conjoint analysis and discrete choice conjoint analysis. As a final point, the mixture of normal assumption in modeling consumer heterogeneity is also favored by marketing society, because it ideally corresponds to the strategic implication of market segmentation. However, a recent argument regarding segmentation encourages us to focus on extreme rather then the homogeneous segments. Therefore, the author will further investigate these different arguments, and explain why the modeling framework proposed in this study is so flexible providing mixed information in targeting the advantages of either extreme or cluster based approach. As expected, it will provide new insights and strategic implications for market segmentation.
胡承民. "Market Segmentation through Self-Organizing Map and Multivariate Cluster Analysis - A Case Study of 3C Stores". Thesis, 1998. http://ndltd.ncl.edu.tw/handle/20087587313118252913.
義守大學
管理科學研究所
86
Market segmentaiton, which is the foundation of selecting marketing strategies, is an indispensable procedure in analyzing market structure. Thus, market segmentation is one of the most important findings in marketing. The conventional research usually employed the "cluster analysis" approaches, which are statistics oriented. Recently, due to its high performance in engineering, artificial neural networks are also applied in management. However, most of these researches use the supervised networks instead of unsupervised networks. Hence, this study dedicates to apply the unsupervised neural network, Self-Organizing Map network, in market segmentation. So far, there are still no suitable criteria for selecting the cluster analysis approach. Therefore, this study tries to use the simulated data, which has been well grouped, as the base for comparing the different clustering approaches. These three approaches are: (1) traditional two-stage cluster analysis, (2) Self-Organizing Map neural network, and (3) proposed two-stage cluster analysis which integrates Self-Organizing Map neural network and K-means. The simulation results showed that both the traditional and proposed two-stage cluster analysis approaches have the similar performance and are better than the single Self-Organizing Map neural network. In order to further testify the proposed approach, a real-fife problem, 3C stores market segmentation, is employed. First, factor analysis technique, or principle component analysis, divided the factors into six factors. Then, the above two mentioned approaches cluster the customers into four distinct groups after examining three alternatives, which are three, four and five groups. The evaluation results showed that the proposed approach has better performance based on Wilds'''''''' Labmda and discriminant analysis.