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Relevant bibliographies by topics / Functional data clustering / Dissertations / Theses

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Dissertations / Theses on the topic 'Functional data clustering'

Author: Grafiati

Published: 4 June 2021

Last updated: 31 January 2023

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1

Baragilly, Mohammed Hussein Hassan. "Clustering multivariate and functional data using spatial rank functions." Thesis, University of Birmingham, 2016. http://etheses.bham.ac.uk//id/eprint/7124/.

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In this work, we consider the problem of determining the number of clusters in the multivariate and functional data, where the data are represented by a mixture model in which each component corresponds to a different cluster without any prior knowledge of the number of clusters. For the multivariate case, we propose a new forward search methodology based on spatial ranks. We also propose a modified algorithm based on the volume of central rank regions. Our numerical examples show that it produces the best results under elliptic symmetry and it outperforms the traditional forward search based

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Karmakar, Saurav. "Statistical Stability and Biological Validity of Clustering Algorithms for Analyzing Microarray Data." Digital Archive @ GSU, 2005. http://digitalarchive.gsu.edu/math_theses/3.

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Simultaneous measurement of the expression levels of thousands to ten thousand genes in multiple tissue types is a result of advancement in microarray technology. These expression levels provide clues about the gene functions and that have enabled better diagnosis and treatment of serious disease like cancer. To solve the mystery of unknown gene functions, biological to statistical mapping is needed in terms of classifying the genes. Here we introduce a novel approach of combining both statistical consistency and biological relevance of the clusters produced by a clustering method. Here we emp

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Vogetseder, Georg. "Functional Analysis of Real World Truck Fuel Consumption Data." Thesis, Halmstad University, School of Information Science, Computer and Electrical Engineering (IDE), 2008. http://urn.kb.se/resolve?urn=urn:nbn:se:hh:diva-1148.

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<p>This thesis covers the analysis of sparse and irregular fuel consumption data of long</p><p>distance haulage articulate trucks. It is shown that this kind of data is hard to analyse with multivariate as well as with functional methods. To be able to analyse the data, Principal Components Analysis through Conditional Expectation (PACE) is used, which enables the use of observations from many trucks to compensate for the sparsity of observations in order to get continuous results. The principal component scores generated by PACE, can then be used to get rough estimates of the trajectories for

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Arnqvist, Per. "Functional clustering methods and marital fertility modelling." Doctoral thesis, Umeå universitet, Institutionen för matematik och matematisk statistik, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-130734.

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This thesis consists of two parts.The first part considers further development of a model used for marital fertility, the Coale-Trussell's fertility model, which is based on age-specific fertility rates. A new model is suggested using individual fertility data and a waiting time after pregnancies. The model is named the waiting model and can be understood as an alternating renewal process with age-specific intensities. Due to the complicated form of the waiting model and the way data is presented, as given in the United Nation Demographic Year Book 1965, a normal approximation is suggested tog

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Jin, Zhongnan. "Statistical Methods for Multivariate Functional Data Clustering, Recurrent Event Prediction, and Accelerated Degradation Data Analysis." Diss., Virginia Tech, 2019. http://hdl.handle.net/10919/102628.

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In this dissertation, we introduce three projects in machine learning and reliability applications after the general introductions in Chapter 1. The first project concentrates on the multivariate sensory data, the second project is related to the bivariate recurrent process, and the third project introduces thermal index (TI) estimation in accelerated destructive degradation test (ADDT) data, in which an R package is developed. All three projects are related to and can be used to solve certain reliability problems. Specifically, in Chapter 2, we introduce a clustering method for multivariate f

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Jiang, Huijing. "Statistical computation and inference for functional data analysis." Diss., Georgia Institute of Technology, 2010. http://hdl.handle.net/1853/37087.

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My doctoral research dissertation focuses on two aspects of functional data analysis (FDA): FDA under spatial interdependence and FDA for multi-level data. The first part of my thesis focuses on developing modeling and inference procedure for functional data under spatial dependence. The methodology introduced in this part is motivated by a research study on inequities in accessibility to financial services. The first research problem in this part is concerned with a novel model-based method for clustering random time functions which are spatially interdependent. A cluster consists of time

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Koomson, Obed. "Performance Assessment of The Extended Gower Coefficient on Mixed Data with Varying Types of Functional Data." Digital Commons @ East Tennessee State University, 2018. https://dc.etsu.edu/etd/3512.

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Clustering is a widely used technique in data mining applications to source, manage, analyze and extract vital information from large amounts of data. Most clustering procedures are limited in their performance when it comes to data with mixed attributes. In recent times, mixed data have evolved to include directional and functional data. In this study, we will give an introduction to clustering with an eye towards the application of the extended Gower coefficient by Hendrickson (2014). We will conduct a simulation study to assess the performance of this coefficient on mixed data whose functio

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8

Fountalis, Ilias. "From spatio-temporal data to a weighted and lagged network between functional domains: Applications in climate and neuroscience." Diss., Georgia Institute of Technology, 2016. http://hdl.handle.net/1853/55008.

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Spatio-temporal data have become increasingly prevalent and important for both science and enterprises. Such data are typically embedded in a grid with a resolution larger than the true dimensionality of the underlying system. One major task is to identify the distinct semi-autonomous functional components of the spatio-temporal system and to infer their interconnections. In this thesis, we propose two methods that identify the functional components of a spatio-temporal system. Next, an edge inference process identifies the possibly lagged and weighted connections between the system’s compone

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9

Li, Han. "Statistical Modeling and Analysis of Bivariate Spatial-Temporal Data with the Application to Stream Temperature Study." Diss., Virginia Tech, 2014. http://hdl.handle.net/10919/70862.

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Water temperature is a critical factor for the quality and biological condition of streams. Among various factors affecting stream water temperature, air temperature is one of the most important factors related to water temperature. To appropriately quantify the relationship between water and air temperatures over a large geographic region, it is important to accommodate the spatial and temporal information of the steam temperature. In this dissertation, I devote effort to several statistical modeling techniques for analyzing bivariate spatial-temporal data in a stream temperature study. I

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10

Jonsson, Per. "Improving Clustering of Gene Expression Patterns." Thesis, University of Skövde, Department of Computer Science, 2000. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-482.

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<p>The central question investigated in this project was whether clustering of gene expression patterns could be done more biologically accurate by providing the clustering technique with additional information about the genes as input besides the expression levels. With the term biologically accurate we mean that the genes should not only be clustered together according to their similarities in expression profiles, but also according to their functional similarity in terms of functional annotation and metabolic pathway. The data was collected at AstraZeneca R&D Mölndal Sweden and the applied

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Khatamian, Yasha. "Investigating the limits of temporal clustering analysis for detecting epileptic activity in functional magnetic resonance imaging data." Thesis, McGill University, 2011. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=96861.

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Precise localization of epileptic activity is a necessity for those patients who may benefit from resective surgery. One common localization technique, EEG functional MRI (EEG-fMRI), localizes activity in an fMRI recording by finding blood oxygen level dependent (BOLD) signal correlates to epileptic events detected in a simultaneously recorded EEG. 2D temporal clustering analysis (2D-TCA) is a relatively new fMRI-based epileptic activity localization technique that breaks BOLD activity into components based on timing, finding epileptic activity without simultaneously recorded EEG. This study e

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12

Freudenberg, Johannes M. "Bayesian Infinite Mixture Models for Gene Clustering and Simultaneous Context Selection Using High-Throughput Gene Expression Data." University of Cincinnati / OhioLINK, 2009. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1258660232.

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Leroy, Arthur. "Multi-task learning models for functional data and application to the prediction of sports performances." Thesis, Université de Paris (2019-....), 2020. http://www.theses.fr/2020UNIP7089.

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Ce manuscrit de thèse est consacré à l’analyse de données fonctionnelles et la définition de modèles multi-tâches pour la régression et la classification non supervisée. L’objectif de ce travail est double et trouve sa motivation dans la problématique d’identification de jeunes sportifs prometteurs pour le sport de haut niveau. Ce contexte, qui offre un fil rouge illustratif des méthodes et algorithmes développés par la suite, soulève la question de l’étude de multiples séries temporelles supposées partager de l’information commune, et généralement observées à pas de temps irréguliers. La méth

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Huo, Shuning. "Bayesian Modeling of Complex High-Dimensional Data." Diss., Virginia Tech, 2020. http://hdl.handle.net/10919/101037.

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With the rapid development of modern high-throughput technologies, scientists can now collect high-dimensional complex data in different forms, such as medical images, genomics measurements. However, acquisition of more data does not automatically lead to better knowledge discovery. One needs efficient and reliable analytical tools to extract useful information from complex datasets. The main objective of this dissertation is to develop innovative Bayesian methodologies to enable effective and efficient knowledge discovery from complex high-dimensional data. It contains two parts—the developme

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Barbosa, Roa Nathalie Andrea. "A data-based approach for dynamic classification of functional scenarios oriented to industrial process plants." Thesis, Toulouse 3, 2016. http://www.theses.fr/2016TOU30245/document.

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L'objectif principal de cette thèse est de développer un algorithme dynamique de partitionnement de données (classification non supervisée ou " clustering " en anglais) qui ne se limite pas à des concepts statiques et qui peut gérer des distributions qui évoluent au fil du temps. Cet algorithme peut être utilisé dans les systèmes de surveillance du processus, mais son application ne se limite pas à ceux-ci. Les contributions de cette thèse peuvent être présentées en trois groupes: 1. Contributions au partitionnement dynamique de données en utilisant : un algorithme de partitionnement dynamique

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Schmutz, Amandine. "Contributions à l'analyse de données fonctionnelles multivariées, application à l'étude de la locomotion du cheval de sport." Thesis, Lyon, 2019. http://www.theses.fr/2019LYSE1241.

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Avec l'essor des objets connectés pour fournir un suivi systématique, objectif et fiable aux sportifs et à leur entraineur, de plus en plus de paramètres sont collectés pour un même individu. Une alternative aux méthodes d'évaluation en laboratoire est l'utilisation de capteurs inertiels qui permettent de suivre la performance sans l'entraver, sans limite d'espace et sans procédure d'initialisation fastidieuse. Les données collectées par ces capteurs peuvent être vues comme des données fonctionnelles multivariées : se sont des entités quantitatives évoluant au cours du temps de façon simultané

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Pešout, Pavel. "Přístupy k shlukování funkčních dat." Doctoral thesis, Vysoká škola ekonomická v Praze, 2007. http://www.nusl.cz/ntk/nusl-77066.

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Classification is a very common task in information processing and important problem in many sectors of science and industry. In the case of data measured as a function of a dependent variable such as time, the most used algorithms may not pattern each of the individual shapes properly, because they are interested only in the choiced measurements. For the reason, the presented paper focuses on the specific techniques that directly address the curve clustering problem and classifying new individuals. The main goal of this work is to develop alternative methodologies through the extension to var

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Ben, slimen Yosra. "Knowledge extraction from huge volume of heterogeneous data for an automated radio network management." Thesis, Lyon, 2018. http://www.theses.fr/2018LYSE2046.

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En vue d’aider les opérateurs mobiles avec la gestion de leurs réseaux d’accès radio, trois modèles sont proposés. Le premier modèle est une approche supervisée pour une prévention des anomalies. Son objectif est de détecter les dysfonctionnements futurs d’un ensemble de cellules en observant les indicateurs clés de performance considérés comme des données fonctionnelles. Par conséquent, en alertant les ingénieurs et les réseaux auto-organisés, les opérateurs mobiles peuvent être sauvés d’une dégradation de performance de leurs réseaux. Le modèle a prouvé son efficacité avec une application su

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Li, Qian. "Approaches to Find the Functionally Related Experiments Based on Enrichment Scores: Infinite Mixture Model Based Cluster Analysis for Gene Expression Data." University of Cincinnati / OhioLINK, 2013. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1378113351.

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Traore, Oumar Issiaka. "Méthodologie de traitement et d'analyse de signaux expérimentaux d'émission acoustique : application au comportement d'un élément combustible en situation accidentelle." Thesis, Aix-Marseille, 2018. http://www.theses.fr/2018AIXM0011/document.

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L’objectif de cette thèse est de contribuer à l’amélioration du processus de dépouillement d’essais de sûreté visant étudier le comportement d'un combustible nucléaire en contexte d’accident d’injection de réactivité (RIA), via la technique de contrôle par émission acoustique. Il s’agit notamment d’identifier clairement les mécanismes physiques pouvant intervenir au cours des essais à travers leur signature acoustique. Dans un premier temps, au travers de calculs analytiques et des simulation numériques conduites au moyen d’une méthode d’éléments finis spectraux, l’impact du dispositif d’essai

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Kashlak, Adam B. "A concentration inequality based statistical methodology for inference on covariance matrices and operators." Thesis, University of Cambridge, 2017. https://www.repository.cam.ac.uk/handle/1810/267833.

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In the modern era of high and infinite dimensional data, classical statistical methodology is often rendered inefficient and ineffective when confronted with such big data problems as arise in genomics, medical imaging, speech analysis, and many other areas of research. Many problems manifest when the practitioner is required to take into account the covariance structure of the data during his or her analysis, which takes on the form of either a high dimensional low rank matrix or a finite dimensional representation of an infinite dimensional operator acting on some underlying function space.

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Juery, Damien. "Classification bayésienne non supervisée de données fonctionnelles en présence de covariables." Thesis, Montpellier 2, 2014. http://www.theses.fr/2014MON20160/document.

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Un des objectifs les plus importants en classification non supervisée est d'extraire des groupes de similarité depuis un jeu de données. Avec le développement actuel du phénotypage où les données sont recueillies en temps continu, de plus en plus d'utilisateurs ont besoin d'outils capables de classer des courbes.Le travail présenté dans cette thèse se fonde sur la statistique bayésienne. Plus précisément, nous nous intéressons à la classification bayésienne non supervisée de données fonctionnelles. Les lois a priori bayésiennes non paramétriques permettent la construction de modèles flexibles

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Cléa, Gomes da Silva Alzennyr. "Dissimilarity fuctions analysis based on dynamic clustering for symbolic data." Universidade Federal de Pernambuco, 2005. https://repositorio.ufpe.br/handle/123456789/2797.

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Made available in DSpace on 2014-06-12T16:01:14Z (GMT). No. of bitstreams: 2 arquivo7274_1.pdf: 1733810 bytes, checksum: 2d9eb7a4489382e5afbf1790810474a0 (MD5) license.txt: 1748 bytes, checksum: 8a4605be74aa9ea9d79846c1fba20a33 (MD5) Previous issue date: 2005<br>A análise de dados simbólicos (Symbolic Data Analysis) é um novo domínio na área de descoberta automática de conhecimento que visa desenvolver métodos para dados descritos por variáveis que podem assumir como valor conjuntos de categorias, intervalos ou distribuições de probabilidade. Essas novas variáveis permitem levar em conta

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Saito, Yasushi. "Functionally homogeneous clustering : a framework for building scalable data-intensive internet services /." Thesis, Connect to this title online; UW restricted, 2001. http://hdl.handle.net/1773/6936.

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Durak, Bahadir. "A Classification Algorithm Using Mahalanobis Distance Clustering Of Data With Applications On Biomedical Data Sets." Master's thesis, METU, 2011. http://etd.lib.metu.edu.tr/upload/12612852/index.pdf.

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The concept of classification is used and examined by the scientific community for hundreds of years. In this historical process, different methods and algorithms have been developed and used. Today, although the classification algorithms in literature use different methods, they are acting on a similar basis. This basis is setting the desired data into classes by using defined properties, with a different discourse<br>an effort to establish a relationship between known features with unknown result. This study was intended to bring a different perspective to this common basis. In this study, n

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Devijver, Emilie. "Modèles de mélange pour la régression en grande dimension, application aux données fonctionnelles." Thesis, Paris 11, 2015. http://www.theses.fr/2015PA112130/document.

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Les modèles de mélange pour la régression sont utilisés pour modéliser la relation entre la réponse et les prédicteurs, pour des données issues de différentes sous-populations. Dans cette thèse, on étudie des prédicteurs de grande dimension et une réponse de grande dimension. Tout d’abord, on obtient une inégalité oracle ℓ1 satisfaite par l’estimateur du Lasso. On s’intéresse à cet estimateur pour ses propriétés de régularisation ℓ1. On propose aussi deux procédures pour pallier ce problème de classification en grande dimension. La première procédure utilise l’estimateur du maximum de vraisemb

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Groth, Philip. "Knowledge management and discovery for genotype/phenotype data." Doctoral thesis, Humboldt-Universität zu Berlin, Mathematisch-Naturwissenschaftliche Fakultät II, 2009. http://dx.doi.org/10.18452/16033.

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Die Untersuchung des Phänotyps bringt z.B. bei genetischen Krankheiten ein Verständnis der zugrunde liegenden Mechanismen mit sich. Aufgrund dessen wurden neue Technologien wie RNA-Interferenz (RNAi) entwickelt, die Genfunktionen entschlüsseln und mehr phänotypische Daten erzeugen. Interpretation der Ergebnisse solcher Versuche ist insbesondere bei heterogenen Daten eine große Herausforderung. Wenige Ansätze haben bisher Daten über die direkte Verknüpfung von Genotyp und Phänotyp hinaus interpretiert. Diese Dissertation zeigt neue Methoden, die Entdeckungen in Phänotypen über Spezies und Meth

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Oesterling, Patrick. "Visual Analysis of High-Dimensional Point Clouds using Topological Abstraction." Doctoral thesis, Universitätsbibliothek Leipzig, 2016. http://nbn-resolving.de/urn:nbn:de:bsz:15-qucosa-203056.

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This thesis is about visualizing a kind of data that is trivial to process by computers but difficult to imagine by humans because nature does not allow for intuition with this type of information: high-dimensional data. Such data often result from representing observations of objects under various aspects or with different properties. In many applications, a typical, laborious task is to find related objects or to group those that are similar to each other. One classic solution for this task is to imagine the data as vectors in a Euclidean space with object variables as dimensions. Utilizing

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You, Xiaozhen. "Principal Component Analysis and Assessment of Language Network Activation Patterns in Pediatric Epilepsy." FIU Digital Commons, 2010. http://digitalcommons.fiu.edu/etd/176.

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This dissertation establishes a novel data-driven method to identify language network activation patterns in pediatric epilepsy through the use of the Principal Component Analysis (PCA) on functional magnetic resonance imaging (fMRI). A total of 122 subjects’ data sets from five different hospitals were included in the study through a web-based repository site designed here at FIU. Research was conducted to evaluate different classification and clustering techniques in identifying hidden activation patterns and their associations with meaningful clinical variables. The results were assessed th

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Yadav, Jyoti. "Fuzzer Test Log Analysis Using Machine Learning : Framework to analyze logs and provide feedback to guide the fuzzer." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-254893.

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In this modern world machine learning and deep learning have become popular choice for analysis and identifying various patterns on data in large volumes. The focus of the thesis work has been on the design of the alternative strategies using machine learning to guide the fuzzer in selecting the most promising test cases. Thesis work mainly focuses on the analysis of the data by using machine learning techniques. A detailed analysis study and work is carried out in multiple phases. First phase is targeted to convert the data into suitable format(pre-processing) so that necessary features can

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Lian, Chunfeng. "Information fusion and decision-making using belief functions : application to therapeutic monitoring of cancer." Thesis, Compiègne, 2017. http://www.theses.fr/2017COMP2333/document.

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La radiothérapie est une des méthodes principales utilisée dans le traitement thérapeutique des tumeurs malignes. Pour améliorer son efficacité, deux problèmes essentiels doivent être soigneusement traités : la prédication fiable des résultats thérapeutiques et la segmentation précise des volumes tumoraux. La tomographie d’émission de positrons au traceur Fluoro- 18-déoxy-glucose (FDG-TEP) peut fournir de manière non invasive des informations significatives sur les activités fonctionnelles des cellules tumorales. Les objectifs de cette thèse sont de proposer: 1) des systèmes fiables pour prédi

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Picat, Marie-Quitterie. "Analyses intégratives de biomarqueurs immunologiques dans les études épidémiologiques. Applications à trois études cliniques." Thesis, Bordeaux, 2015. http://www.theses.fr/2015BORD0144/document.

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Les processus biologiques sont nombreux et leurs interactions complexes. Les mesures de cesphénomènes génèrent des biomarqueurs multiples. Ainsi, l’épidémiologie doit évoluer dans cecontexte de données complexes et de nature multidimensionnelle. Les maladies du systèmeimmunitaire et les troubles immunologiques qui leur sont associés constituent un bon exemplede pathologies où les questions clinico-épidémiologiques sont de plus en plus complexes,nécessitant des méthodes biostatistiques et épidémiologiques adaptées. Dans cette thèsed’Université, des méthodes permettant de prendre en compte les d

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Hunter, Brandon. "Channel Probing for an Indoor Wireless Communications Channel." BYU ScholarsArchive, 2003. https://scholarsarchive.byu.edu/etd/64.

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The statistics of the amplitude, time and angle of arrival of multipaths in an indoor environment are all necessary components of multipath models used to simulate the performance of spatial diversity in receive antenna configurations. The model presented by Saleh and Valenzuela, was added to by Spencer et. al., and included all three of these parameters for a 7 GHz channel. A system was built to measure these multipath parameters at 2.4 GHz for multiple locations in an indoor environment. Another system was built to measure the angle of transmission for a 6 GHz channel. The addition of th

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Kuo, Ling-Cheng, and 郭令証. "K-centres Functional Clustering of Multivariate Functional Data." Thesis, 2015. http://ndltd.ncl.edu.tw/handle/bshh6e.

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碩士<br>淡江大學<br>統計學系碩士班<br>103<br>Cluster analysis of multivariate functional data is an important issue in real applications. In this study, we propose a novel k-centres multivariate functional clustering (mKCFC) algorithm for the multivariate functional data. The proposed approach is an extension of the k-centres functional clustering method (Chiou and Li, 2007), which is proposed for the univariate functional data, and can take the means and modes of variation differentials among clusters of each variable into account simultaneously. The mKCFC approach adopts a weighted distance for clusterin

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CHEN, HAN-CHIEH, and 陳涵傑. "Self-Updating Process for Functional Data Clustering." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/6w4fjs.

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碩士<br>國立臺北大學<br>統計學系<br>107<br>The self-updating process (SUP) performs clustering on the basis of samples’movements according to between-sample associations. It has been shown that SUP is competitive in clustering data with a large number of clusters and data with noise.In this paper we will present an extension of SUP to functional data clustering. We represent functional data by eigenfunctions from functional principal component analysis (FPCA). At the initial iteration, each sample curve is represented by a set of eigenfunctions. In the end of the iteration, samples that are represented by

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Tzeng, Sheng-Li, and 曾聖澧. "Functional Data Clustering Based on Spline Functions and Random-Effects Models." Thesis, 2017. http://ndltd.ncl.edu.tw/handle/958rqg.

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博士<br>中國醫藥大學<br>公共衛生學系博士班<br>106<br>Many studies measure the same type of information longitudinally on the same subject at multiple time points, and clustering of such functional data has many important applications. We propose a novel and easy method to implement dissimilarity measure for functional data clustering based on smoothing splines and smoothing parameter commutation. This method handles data observed at regular or irregular time points in the same way. We measure the dissimilarity between subjects based on varying curve estimates with pairwise commutation of smoothing parameters.

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Wang, Ling-hui, and 王鈴慧. "Price Analysis of Agricultural Products Using Functional Data Clustering." Thesis, 2014. http://ndltd.ncl.edu.tw/handle/41313861919919389733.

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碩士<br>國立臺灣科技大學<br>工業管理系<br>102<br>Understanding crop seasonal effect and price trend is an important decision making for customers, crop farmers, and retailers. This research applied functional data clustering method to analyze crop price trend and variation. 15-year Taiwan agriculture crop price data were collected. The time-series data was first converted to functional data and the smoothing method was applied to obtain the function to represent each price curve. Then, Principal Component Analysis (PCA) was used to investigate the variation among the 15-year data. The hierarchical clustering

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Li, Cheng-Long, and 李承龍. "Functional clustering with an applications to electricity consumption data." Thesis, 2016. http://ndltd.ncl.edu.tw/handle/14217277809877160161.

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碩士<br>國立中興大學<br>統計學研究所<br>104<br>Avoid unnecessary waste of electric energy consumption, effective power control is commanded. Yao et al. (2005) provide Principal Analysis by Conditional Expectation (PACE), a version of functional principal components analysis (FPCA). Motivated by Yao et al. (2005), we apply PACE to reduce the number of features and rebuild data curves. We find the characteristic such as seasonal or holiday by using k-Center functional clustering, k-Means and Hierarchical clustering.

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Alexiuk, Mark Douglas. "Spatio-temporal fuzzy clustering of functional magnetic resonance imaging data." 2007. http://hdl.handle.net/1993/20312.

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LIN, JIA-YING, and 林佳瑩. "Functional Data Clustering by Self-Updating Process using Geometric Properties." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/5drkx3.

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碩士<br>國立臺北大學<br>統計學系<br>106<br>This paper proposes the use of self-updating process (SUP) on functional data clustering. The first and the second differentiation are considered to capture the geometric properties of the curves, based on which the proximity between curves is measured. The self-updating process uses the above proximity to determine the movements of curves in the sample space. In the end of the process, curves that converge to the same position are identified as in the same cluster. The performance of SUP using geometric properties are evaluated by simulations and the analysis of

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YANG, CHING-WEN, and 楊景雯. "Self-Updating Process with B-splines on Functional Data Clustering." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/23uyn2.

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碩士<br>國立臺北大學<br>統計學系<br>106<br>The self-updating process (SUP) is competitive in clustering data with noise, data with a large number of clusters, and unbalanced data. This paper presents an extension of SUP to functional data clustering by the use of B-spline basis functions. The curves in data are first represented by B-spline functions, then the updating process is to perform clustering in the B-spline space. This paper provides comparison results between the proposed extension of SUP and other existing methods for functional data clustering.

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Chan, Hsin-Yu, and 詹欣諭. "Application of functional data clustering to the study ofTaiwan air quality." Thesis, 2013. http://ndltd.ncl.edu.tw/handle/90373827483704312535.

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碩士<br>淡江大學<br>統計學系碩士班<br>101<br>In recent years, air pollution is getting worse and becomes an important issue in the world. The severity of air pollution is affected not only by urbanization but also climatic differences. For instance, the airflow diffuser or chemical reaction leads to cumulative pollutant and flow across the counties. In this study, we aim to investigate the distribution structures of several air pollutants in Taiwan through a cluster analysis. We apply the subspace projected functional clustering (SPFC) algorithm proposed by Li and Chiou (2011) to the 2011 air pollution dat

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Mosesova, Sofia. "Flexible Mixed-Effect Modeling of Functional Data, with Applications to Process Monitoring." Thesis, 2007. http://hdl.handle.net/10012/3104.

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High levels of automation in manufacturing industries are leading to data sets of increasing size and dimension. The challenge facing statisticians and field professionals is to develop methodology to help meet this demand. Functional data is one example of high-dimensional data characterized by observations recorded as a function of some continuous measure, such as time. An application considered in this thesis comes from the automotive industry. It involves a production process in which valve seats are force-fitted by a ram into cylinder heads of automobile engines. For each insertion, the f

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Chen, Tianbo. "Spectral Density Function Estimation with Applications in Clustering and Classification." Diss., 2019. http://hdl.handle.net/10754/631281.

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Spectral density function (SDF) plays a critical role in spatio-temporal data analysis, where the data are analyzed in the frequency domain. Although many methods have been proposed for SDF estimation, real-world applications in many research fields, such as neuroscience and environmental science, call for better methodologies. In this thesis, we focus on the spectral density functions for time series and spatial data, develop new estimation algorithms, and use the estimators as features for clustering and classification purposes. The first topic is motivated by clustering electroencephalog

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Chiang, Chia-Tung, and 江家彤. "Study effectiveness of k-means clustering of functional data: functional principal component scores feature and mean curve feature." Thesis, 2011. http://ndltd.ncl.edu.tw/handle/95180268728241580134.

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碩士<br>國立臺灣大學<br>數學研究所<br>99<br>Organizing functional data into sensible groupings is one of the most fundamental modes of understanding and learning the underlying mechanism generating functional data. Clustering analysis is often employed to search for homogeneous subgroups of individuals in a data set. In Abraham et al. (2003, Scandinavian Journal of Statistics), they start with feature extraction on the mean function and use k-means clustering procedure to determine the clusters. In Peng and Muller (2008, Annals of Applied Statistics), they assume common mean function for all units and star

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Huang, Chih-Huang, and 黃智煌. "Ticket Sales Prediction of Entertainment Show Using Functional Data Clustering and Artificial Neural Network." Thesis, 2016. http://ndltd.ncl.edu.tw/handle/w8w9u5.

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碩士<br>國立臺灣科技大學<br>工業管理系<br>104<br>The sales performance of an entertainment show or concert tickets not only reflect profit of the business but also represents the popularity of the event. Predicting or forecasting the ticket sales performance before or during the ticket on sale is very important for the organization which hosts entertainment show event. In this research, a ticket sales prediction model was developed to predict the percentage of box office (ticket sales) of each price ranges based on the historical sales performance. In this research, a method called “Artificial Neural Network

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Ming-HungChen and 陳明宏. "Spatial Testing and Spatial Clustering with Applications to Wafer Bin Map and Functional MRI Data." Thesis, 2010. http://ndltd.ncl.edu.tw/handle/20976023015398987194.

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碩士<br>國立成功大學<br>統計學系碩博士班<br>98<br>Spatial smoothing, spatial testing, and spatial clustering are often applied to determine the spatial dependence and find the important spatial region in the field of image analysis. In this thesis, we will modify the above statistical methods to analysis two real data sets. They are a two dimensional Wafer Bin Map (WBM) and a four dimensional (three dimensions in space plus one dimension in time) facial recognition functional Magnetic Resonance Imaging (fMRI) data. Defect pattern recognition of WBM is an important issue for semiconductor fabrication industry

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Barradas, Isabel Francisca Sota Machado. "New metods for the investigation of dynamics of functional magnetic resonance imaging data." Master's thesis, 2018. http://hdl.handle.net/10451/34697.

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Tese de mestrado integrado, Engenharia Biomédica e Biofísica (Sinais e Imagens Médicas) Universidade de Lisboa, Faculdade de Ciências, 2018<br>O estudo in vivo das diversas regiões cerebrais e da forma como estão conectadas tem vindo a beneficiar dos avanços da neuroimagiologia. Este trabalho foca-se na análise da actividade cerebral, razão pela qual é utilizada a ressonância magnética funcional (fMRI, do inglês functional magnetic resonance imaging), uma técnica não-invasiva que permite o mapeamento indirecto da actividade cerebral. Em consequência da actividade neuronal, a resposta hemodinâm

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Wong, Chon-Long, and 黃春龍. "Applications of Mass Spectrometry :(1)Rapid Identify Club Drugs by ELDI-Functional Data-Dependent Tandem MS Approach(2)Differentiation and Clustering Analysis of Acinetobacter Species by MALDI - TOF MS." Thesis, 2011. http://ndltd.ncl.edu.tw/handle/07407093167235172335.

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碩士<br>慈濟大學<br>醫學生物技術研究所<br>99<br>Illicit drug use is increasing at an alerting rate all over the world; millions of people get addicted to some kind of drug every year.　Drug misuse or abuse can result in increased risk of health, personal, legal, environmental problems and also contribute offensive activities like increased crime,　violence and traffic hazards.　Certainly, the consequences of illicit drug use impact the entire criminal justice system and medical systems. Even, early identification and intervention is the key to successfully handling these types of problems. Currently, analysis o

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Adjogou, Adjobo Folly Dzigbodi. "Analyse statistique de données fonctionnelles à structures complexes." Thèse, 2017. http://hdl.handle.net/1866/20581.

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