Дисертації з теми "Density clustering"
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Albarakati, Rayan. "Density Based Data Clustering." CSUSB ScholarWorks, 2015. https://scholarworks.lib.csusb.edu/etd/134.
Повний текст джерелаErdem, Cosku. "Density Based Clustering Using Mathematical Morphology." Master's thesis, METU, 2006. http://etd.lib.metu.edu.tr/upload/12608264/index.pdf.
Повний текст джерелаDensity Based Clustering Using Mathematical Morphology"
(DBCM) algorithm as an effective clustering method for extracting arbitrary shaped clusters of noisy numerical data in a reasonable time. This algorithm is predicated on the analogy between images and data warehouses. It applies grayscale morphology which is an image processing technique on multidimensional data. In this study we evaluated the performance of the proposed algorithm on both synthetic and real data and observed that the algorithm produces successful and interpretable results with appropriate parameters. In addition, we computed the computational complexity to be linear on number of data points for low dimensional data and exponential on number of dimensions for high dimensional data mainly due to the morphology operations.
Holzapfel, Klaus. "Density-based clustering in large-scale networks." [S.l.] : [s.n.], 2006. http://deposit.ddb.de/cgi-bin/dokserv?idn=979979943.
Повний текст джерелаPark, Ju-Hyun Dunson David B. "Bayesian density regression and predictor-dependent clustering." Chapel Hill, N.C. : University of North Carolina at Chapel Hill, 2008. http://dc.lib.unc.edu/u?/etd,1821.
Повний текст джерелаTitle from electronic title page (viewed Dec. 11, 2008). "... in partial fulfillment of the requirements for the degree of Doctor of Philosophy in the Department of Biostatistics, School of Public Health." Discipline: Biostatistics; Department/School: Public Health.
Kröger, Peer. "Coping With New Challengens for Density-Based Clustering." Diss., lmu, 2004. http://nbn-resolving.de/urn:nbn:de:bvb:19-23966.
Повний текст джерелаMai, Son. "Density-based algorithms for active and anytime clustering." Diss., Ludwig-Maximilians-Universität München, 2014. http://nbn-resolving.de/urn:nbn:de:bvb:19-175337.
Повний текст джерелаDatenintensive Anwendungen wie Biologie, Medizin und Neurowissenschaften erfordern effektive und effiziente Data-Mining-Technologien. Erweiterte Methoden der Datenerfassung erzeugen stetig wachsende Datenmengen und Komplexit\"at. In den letzten Jahrzehnten hat sich daher ein Bedarf an neuen Data-Mining-Technologien f\"ur komplexe Daten ergeben. In dieser Arbeit konzentrieren wir uns auf die Data-Mining-Aufgabe des Clusterings, in der Objekte in verschiedenen Gruppen (Cluster) getrennt werden, so dass Objekte in einem Cluster untereinander viel \"ahnlicher sind als Objekte in verschiedenen Clustern. Insbesondere betrachten wir dichtebasierte Clustering-Algorithmen und ihre Anwendungen in der Biomedizin. Der Kerngedanke des dichtebasierten Clustering-Algorithmus DBSCAN ist, dass jedes Objekt in einem Cluster eine bestimmte Anzahl von anderen Objekten in seiner Nachbarschaft haben muss. Im Vergleich mit anderen Clustering-Algorithmen hat DBSCAN viele attraktive Vorteile, zum Beispiel kann es Cluster mit beliebiger Form erkennen und ist robust gegen\"uber Ausrei{\ss}ern. So hat DBSCAN in den letzten Jahrzehnten gro{\ss}es Forschungsinteresse mit vielen Erweiterungen und Anwendungen auf sich gezogen. Im ersten Teil dieser Arbeit wollen wir auf die Entwicklung neuer Algorithmen eingehen, die auf dem DBSCAN Paradigma basieren, um mit den neuen Herausforderungen der komplexen Daten, insbesondere teurer Abstandsma{\ss}e und unvollst\"andiger Verf\"ugbarkeit der Distanzmatrix umzugehen. Wie viele andere Clustering-Algorithmen leidet DBSCAN an schlechter Per- formanz, wenn es teuren Abstandsma{\ss}en f\"ur komplexe Daten gegen\"uber steht. Um dieses Problem zu l\"osen, schlagen wir einen neuen Algorithmus vor, der auf dem DBSCAN Paradigma basiert, genannt Anytime Density-based Clustering (A-DBSCAN), der mit einem Anytime Schema funktioniert. Im Gegensatz zu dem urspr\"unglichen Schema DBSCAN, erzeugt der Algorithmus A-DBSCAN zuerst eine schnelle Ann\"aherung des Clusterings-Ergebnisses und verfeinert dann kontinuierlich das Ergebnis im weiteren Verlauf. Experten k\"onnen den Algorithmus unterbrechen, die Ergebnisse pr\"ufen und w\"ahlen zwischen (1) Anhalten des Algorithmus zu jeder Zeit, wann immer sie mit dem Ergebnis zufrieden sind, um Laufzeit sparen und (2) Fortsetzen des Algorithmus, um bessere Ergebnisse zu erzielen. Eine solche Art eines "Anytime Schemas" ist in der Literatur als eine sehr n\"utzliche Technik erprobt, wenn zeitaufwendige Problemen anfallen. Wir stellen auch eine erweiterte Version von A-DBSCAN als A-DBSCAN-XS vor, die effizienter und effektiver als A-DBSCAN beim Umgang mit teuren Abstandsma{\ss}en ist. Da DBSCAN auf der Kardinalit\"at der Nachbarschaftsobjekte beruht, ist es notwendig, die volle Distanzmatrix auszurechen. F\"ur komplexe Daten sind diese Distanzen in der Regel teuer, zeitaufwendig oder sogar unm\"oglich zu errechnen, aufgrund der hohen Kosten, einer hohen Zeitkomplexit\"at oder verrauschten und fehlende Daten. Motiviert durch diese m\"oglichen Schwierigkeiten der Berechnung von Entfernungen zwischen Objekten, schlagen wir einen anderen Ansatz f\"ur DBSCAN vor, namentlich Active Density-based Clustering (Act-DBSCAN). Bei einer Budgetbegrenzung B, darf Act-DBSCAN nur bis zu B ideale paarweise Distanzen verwenden, um das gleiche Ergebnis zu produzieren, wie wenn es die gesamte Distanzmatrix zur Hand h\"atte. Die allgemeine Idee von Act-DBSCAN ist, dass es aktiv die erfolgversprechendsten Paare von Objekten w\"ahlt, um die Abst\"ande zwischen ihnen zu berechnen, und versucht, sich so viel wie m\"oglich dem gew\"unschten Clustering mit jeder Abstandsberechnung zu n\"ahern. Dieses Schema bietet eine effiziente M\"oglichkeit, die Gesamtkosten der Durchf\"uhrung des Clusterings zu reduzieren. So schr\"ankt sie die potenzielle Schw\"ache des DBSCAN beim Umgang mit dem Distance Sparseness Problem von komplexen Daten ein. Als fundamentaler Clustering-Algorithmus, hat dichte-basiertes Clustering viele Anwendungen in den unterschiedlichen Bereichen. Im zweiten Teil dieser Arbeit konzentrieren wir uns auf eine Anwendung des dichte-basierten Clusterings in den Neurowissenschaften: Die Segmentierung der wei{\ss}en Substanz bei Faserbahnen im menschlichen Gehirn, die vom Diffusion Tensor Imaging (DTI) erfasst werden. Wir schlagen ein Modell vor, um die \"Ahnlichkeit zwischen zwei Fasern als einer Kombination von struktureller und konnektivit\"atsbezogener \"Ahnlichkeit von Faserbahnen zu beurteilen. Verschiedene Abstandsma{\ss}e aus Bereichen wie dem Time-Sequence Mining werden angepasst, um die strukturelle \"Ahnlichkeit von Fasern zu berechnen. Dichte-basiertes Clustering wird als Segmentierungsalgorithmus verwendet. Wir zeigen, wie A-DBSCAN und A-DBSCAN-XS als neuartige L\"osungen f\"ur die Segmentierung von sehr gro{\ss}en Faserdatens\"atzen verwendet werden, und bieten innovative Funktionen, um Experten w\"ahrend des Fasersegmentierungsprozesses zu unterst\"utzen.
Dixit, Siddharth. "Density Based Clustering using Mutual K-Nearest Neighbors." University of Cincinnati / OhioLINK, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1447690719.
Повний текст джерелаTuhin, RASHEDUL AMIN. "Securing GNSS Receivers with a Density-based Clustering Algorithm." Thesis, KTH, Kommunikationsnät, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-182117.
Повний текст джерелаBraune, Christian [Verfasser]. "Skeleton-based validation for density-based clustering / Christian Braune." Magdeburg : Universitätsbibliothek Otto-von-Guericke-Universität, 2018. http://d-nb.info/1220035653/34.
Повний текст джерелаLilje, Per Vidar Barth. "Large-scale density and velocity fields in the Universe." Thesis, University of Cambridge, 1988. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.254245.
Повний текст джерелаHinneburg, Alexander. "Density based clustering in large databases using projections and visualizations." [S.l. : s.n.], 2002. http://deposit.ddb.de/cgi-bin/dokserv?idn=967390583.
Повний текст джерелаBugrien, Jamal B. "Robust approaches to clustering based on density estimation and projection." Thesis, University of Leeds, 2005. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.418939.
Повний текст джерелаSantiago, Rafael de. "Efficient modularity density heuristics in graph clustering and their applications." reponame:Biblioteca Digital de Teses e Dissertações da UFRGS, 2017. http://hdl.handle.net/10183/164066.
Повний текст джерелаEldridge, Justin Eldridge. "Clustering Consistently." The Ohio State University, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=osu1512070374903249.
Повний текст джерелаAl-Azab, Fadwa Gamal Mohammed. "An Improved Density-Based Clustering Algorithm Using Gravity and Aging Approaches." Thesis, Université d'Ottawa / University of Ottawa, 2015. http://hdl.handle.net/10393/31994.
Повний текст джерелаKannamareddy, Aruna Sai. "Density and partition based clustering on massive threshold bounded data sets." Kansas State University, 2017. http://hdl.handle.net/2097/35467.
Повний текст джерелаDepartment of Computing and Information Sciences
William H. Hsu
The project explores the possibility of increasing efficiency in the clusters formed out of massive data sets which are formed using threshold blocking algorithm. Clusters thus formed are denser and qualitative. Clusters that are formed out of individual clustering algorithms alone, do not necessarily eliminate outliers and the clusters generated can be complex, or improperly distributed over the data set. The threshold blocking algorithm, a current research paper from Michael Higgins of Statistics Department on other hand, in comparison with existing algorithms performs better in forming the dense and distinctive units with predefined threshold. Developing a hybridized algorithm by implementing the existing clustering algorithms to re-cluster these units thus formed is part of this project. Clustering on the seeds thus formed from threshold blocking Algorithm, eases the task of clustering to the existing algorithm by eliminating the overhead of worrying about the outliers. Also, the clusters thus generated are more representative of the whole. Also, since the threshold blocking algorithm is proven to be fast and efficient, we now can predict a lot more decisions from large data sets in less time. Predicting the similar songs from Million Song Data Set using such a hybridized algorithm is considered as the data set for the evaluation of this goal.
Teodoro, Luís Filipe Alves. "The density and velocity fields of the local universe." Thesis, Durham University, 1999. http://etheses.dur.ac.uk/4550/.
Повний текст джерелаGuan, C. "Evolutionary and swarm algorithm optimized density-based clustering and classification for data analytics." Thesis, University of Liverpool, 2017. http://livrepository.liverpool.ac.uk/3021212/.
Повний текст джерелаTour, Samir R. "Parallel Hybrid Clustering using Genetic Programming and Multi-Objective Fitness with Density(PYRAMID)." NSUWorks, 2006. http://nsuworks.nova.edu/gscis_etd/886.
Повний текст джерелаHemerich, Daiane. "Spatio-temporal data mining in palaeogeographic data with a density-based clustering algorithm." Pontifícia Universidade Católica do Rio Grande do Sul, 2014. http://hdl.handle.net/10923/5929.
Повний текст джерелаThe usefulness of data mining and the process of Knowledge Discovery in Databases (KDD) has increased its importance as grows the volume of data stored in large repositories. A promising area for knowledge discovery concerns oil prospection, in which data used differ both from traditional and geographical data. In palaeogeographic data, temporal dimension is treated according to the geologic time scale, while the spatial dimension is related to georeferenced data, i. e. , latitudes and longitudes on Earth’s surface. This approach differs from that presented by spatio-temporal data mining algorithms found in literature, arising the need to evolve the existing ones to the context of this research. This work presents the development of a solution to employ a density-based spatio-temporal algorithm for mining palaeogeographic data on the Earth’s surface. An evolved version of the ST-DBSCAN algorithm was implemented in Java language making use of Weka API, where improvements were carried out in order to allow the data mining algorithm to solve a variety of research problems identified. A set of experiments that validate the proposed implementations on the algorithm are presented in this work. The experiments show that the solution developed allow palaeogeographic data mining by applying appropriate formulas for calculating distances over the Earth’s surface and, at the same time, treating the temporal dimension according to the geologic time scale.
O uso da mineração de dados e do processo de descoberta de conhecimento em banco de dados (Knowledge Discovery in Databases (KDD)) vem crescendo em sua importância conforme cresce o volume de dados armazenados em grandes repositórios. Uma área promissora para descoberta do conhecimento diz respeito à prospecção de petróleo, onde os dados usados diferem tanto de dados tradicionais como de dados geográficos. Nesses dados, a dimensão temporal é tratada de acordo com a escala de tempo geológico, enquanto a escala espacial é relacionada a dados georeferenciados, ou seja, latitudes e longitudes projetadas na superfície terrestre. Esta abordagem difere da adotada em algoritmos de mineração espaço-temporal presentes na literatura, surgindo assim a necessidade de evolução dos algoritmos existentes a esse contexto de pesquisa. Este trabalho apresenta o desenvolvimento de uma solução para uso do algoritmo de mineração de dados espaço-temporais baseado em densidade ST-DBSCAN para mineração de dados paleogeográficos na superfície terrestre. O algoritmo foi implementado em linguagem de programação Java utilizando a API Weka, onde aperfeiçoamentos foram feitos a fim de permitir o uso de mineração de dados na solução de problemas de pesquisa identificados. Como resultados, são apresentados conjuntos de experimentos que validam as implementações propostas no algoritmo. Os experimentos demonstram que a solução desenvolvida permite a mineração de dados paleogeográficos com a aplicação de fórmulas apropriadas para cálculo de distâncias sobre a superfície terrestre e, ao mesmo tempo, tratando a dimensão temporal de acordo com a escala de tempo geológico.
Piekenbrock, Matthew J. "Discovering Intrinsic Points of Interest from Spatial Trajectory Data Sources." Wright State University / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=wright1527160689990512.
Повний текст джерелаMai, Son Thai [Verfasser], and Christian [Akademischer Betreuer] Böhm. "Density-based algorithms for active and anytime clustering / Son Thai Mai. Betreuer: Christian Böhm." München : Universitätsbibliothek der Ludwig-Maximilians-Universität, 2014. http://d-nb.info/106000710X/34.
Повний текст джерелаWang, Haolei. "Using density-based clustering to improve skeleton embedding in the Pinocchio automatic rigging system." Thesis, Kansas State University, 2012. http://hdl.handle.net/2097/15102.
Повний текст джерелаDepartment of Computing and Information Sciences
William H. Hsu
Automatic rigging is a targeting approach that takes a 3-D character mesh and an adapted skeleton and automatically embeds it into the mesh. Automating the embedding step provides a savings over traditional character rigging approaches, which require manual guidance, at the cost of occasional errors in recognizing parts of the mesh and aligning bones of the skeleton with it. In this thesis, I examine the problem of reducing such errors in an auto-rigging system and apply a density-based clustering algorithm to correct errors in a particular system, Pinocchio (Baran & Popovic, 2007). I show how the density-based clustering algorithm DBSCAN (Ester et al., 1996) is able to filter out some impossible vertices to correct errors at character extremities (hair, hands, and feet) and those resulting from clothing that hides extremities such as legs.
Johnson, Eric. "Density-Based Clustering of High-Dimensional DNA Fingerprints for Library-Dependent Microbial Source Tracking." DigitalCommons@CalPoly, 2015. https://digitalcommons.calpoly.edu/theses/1511.
Повний текст джерелаMaier, Joshua. "PERFORMANCE STUDY OF SOW-AND-GROW: A NEW CLUSTERING ALGORITHM FOR BIG DATA." OpenSIUC, 2020. https://opensiuc.lib.siu.edu/theses/2669.
Повний текст джерелаCourjault-Rade, Vincent. "Ballstering : un algorithme de clustering dédié à de grands échantillons." Thesis, Toulouse 3, 2018. http://www.theses.fr/2018TOU30126/document.
Повний текст джерелаBallstering belongs to the machine learning methods that aim to group in classes a set of objects that form the studied dataset, without any knowledge of true classes within it. This type of methods, of which k-means is one of the most famous representative, are named clustering methods. Recently, a new clustering algorithm "Fast Density Peak Clustering" (FDPC) has aroused great interest from the scientific community for its innovating aspect and its efficiency on non-concentric distributions. However this algorithm showed a such complexity that it can't be applied with ease on large datasets. Moreover, we have identified several weaknesses that impact the quality results and the presence of a general parameter dc difficult to choose while having a significant impact on the results. In view of those limitations, we reworked the principal idea of FDPC in a new light and modified it successively to finally create a distinct algorithm that we called Ballstering. The work carried out during those three years can be summarised by the conception of this clustering algorithm especially designed to be effective on large datasets. As its Precursor, Ballstering works in two phases: An estimation density phase followed by a clustering step. Its conception is mainly based on a procedure that handle the first step with a lower complexity while avoiding at the same time the difficult choice of dc, which becomes automatically defined according to local density. We name ICMDW this procedure which represent a consistent part of our contributions. We also overhauled cores definitions of FDPC and entirely reworked the second phase (relying on the graph structure of ICMDW's intermediate results), to finally produce an algorithm that overcome all the limitations that we have identified
CASSIANO, KEILA MARA. "TIME SERIES ANALYSIS USING SINGULAR SPECTRUM ANALYSIS (SSA) AND BASED DENSITY CLUSTERING OF THE COMPONENTS." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2014. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=24787@1.
Повний текст джерелаEsta tese propõe a utilização do DBSCAN (Density Based Spatial Clustering of Applications with Noise) para separar os componentes de ruído na fase de agrupamento das autotriplas da Análise Singular Espectral (SSA) de Séries Temporais. O DBSCAN é um método moderno de clusterização (revisto em 2013) e especialista em identificar ruído através de regiões de menor densidade. O método de agrupamento hierárquico até então é a última inovação na separação de ruído na abordagem SSA, implementado no pacote R- SSA. No entanto, o método de agrupamento hierárquico é muito sensível a ruído, não é capaz de separá-lo corretamente, não deve ser usado em conjuntos com diferentes densidades e não funciona bem no agrupamento de séries temporais de diferentes tendências, ao contrário dos métodos de aglomeração à base de densidade que são eficazes para separar o ruído a partir dos dados e dedicados para trabalhar bem em dados a partir de diferentes densidades. Este trabalho mostra uma melhor eficiência de DBSCAN sobre os outros métodos já utilizados nesta etapa do SSA, garantindo considerável redução de ruídos e proporcionando melhores previsões. O resultado é apoiado por avaliações experimentais realizadas para séries simuladas de modelos estacionários e não estacionários. A combinação de metodologias proposta também foi aplicada com sucesso na previsão de uma série real de velocidade do vento.
This thesis proposes using DBSCAN (Density Based Spatial Clustering of Applications with Noise) to separate the noise components of eigentriples in the grouping stage of the Singular Spectrum Analysis (SSA) of Time Series. The DBSCAN is a modern (revised in 2013) and expert method at identify noise through regions of lower density. The hierarchical clustering method was the last innovation in noise separation in SSA approach, implemented on package R-SSA. However, is repeated in the literature that the hierarquical clustering method is very sensitive to noise, is unable to separate it correctly, and should not be used in clusters with varying densities and neither works well in clustering time series of different trends. Unlike, the methods of density based clustering are effective in separating the noise from the data and dedicated to work well on data from different densities This work shows better efficiency of DBSCAN over the others methods already used in this stage of SSA, because it allows considerable reduction of noise and provides better forecasting. The result is supported by experimental evaluations realized for simulated stationary and non-stationary series. The proposed combination of methodologies also was applied successfully to forecasting real series of wind s speed.
Song, Juhee. "Bootstrapping in a high dimensional but very low sample size problem." Texas A&M University, 2003. http://hdl.handle.net/1969.1/3853.
Повний текст джерелаJuan, Rovira Enric. "Analytic derivation of non-linear dark matter clustering from the filtering of the primordial density field." Doctoral thesis, Universitat de Barcelona, 2016. http://hdl.handle.net/10803/395192.
Повний текст джерелаEn aquesta tesi demostrem com les propietats dels halos de matèria fosca poden ser derivades directament del camp primordial de densitat si s'usa un filtre adequat. En aquest marc, desenvolupem el formalisme CUSP (ConflUent System of Peak trajectories). Amb aquesta tesi completem aquest tractament analític de la formació no lineal d'estructura en el nostre Univers. En primer lloc, fem un resum del formalisme i les seves bases teòriques, explicant com pot ser usat per trobar les propietats típiques dels halos. Alhora demostrem l'existència d'una correspondència unívoca entre pics de densitat i halos. També es demostra que les propietats dels halos relaxats són les mateixes tant si s'han format per fusions o per acreció pura, ja que aquestes només depenen de les propietats dels pics progenitors de major escala. D'aquesta manera, entenem perquè les propietats típiques dels halos de matèria fosca depenen només de la seva massa i del temps. Un cop establert el formalisme CUSP, aquest ha estat usat, en primer lloc, per estudiar el creixement dels halos de matèria fosca. En particular, hem demostrat que els halos creixen de dins cap a fora, punt crucial en el desenvolupament del formalisme CUSP. També hem estudiat els dominis de validesa de la configuració de tipus NFW i Einasto per als perfils de densitat dels halos, establint alhora unes relacions analítiques per a les relacions massa-concentració-forma. Finalment, també hem aplicat el formalisme CUSP per a estudiar les funcions de massa i multiplicitat, i la dependència d'aquestes amb la definició de massa usada. Hem demostrat que l'algoritme de cerca d'halos FOF (0.2) (molt usat en simulacions numèriques) és equivalent a la definició de sobredensitat virial. També hem demostrat el motiu pel qual els radis virials dels halos són similars als top-hat del col·lapse esfèric i perquè la funció de masses dels halos és tan similar a la de Press-Shechter. Finalment, hem explicat el motiu pel qual la funció de multiplicitat dels halos és pràcticament universal en els dos casos equivalents descrits anterioment.
Wang, Xing. "Time Dependent Kernel Density Estimation: A New Parameter Estimation Algorithm, Applications in Time Series Classification and Clustering". Scholar Commons, 2016. http://scholarcommons.usf.edu/etd/6425.
Повний текст джерелаRomild, Ulla. "Essays on Distance Based (Non-Euclidean) Tests for Spatial Clustering in Inhomogeneous Populations : Adjusting for the Inhomogeneity through the Distance Used." Doctoral thesis, Uppsala : Acta Universitatis Upsaliensis, 2006. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-6829.
Повний текст джерелаSebbar, Mehdi. "On unsupervised learning in high dimension." Thesis, Université Paris-Saclay (ComUE), 2017. http://www.theses.fr/2017SACLG003/document.
Повний текст джерелаIn this thesis, we discuss two topics, high-dimensional clustering on the one hand and estimation of mixing densities on the other. The first chapter is an introduction to clustering. We present various popular methods and we focus on one of the main models of our work which is the mixture of Gaussians. We also discuss the problems with high-dimensional estimation (Section 1.3) and the difficulty of estimating the number of clusters (Section 1.1.4). In what follows, we present briefly the concepts discussed in this manuscript. Consider a mixture of $K$ Gaussians in $RR^p$. One of the common approaches to estimate the parameters is to use the maximum likelihood estimator. Since this problem is not convex, we can not guarantee the convergence of classical methods such as gradient descent or Newton's algorithm. However, by exploiting the biconvexity of the negative log-likelihood, the iterative 'Expectation-Maximization' (EM) procedure described in Section 1.2.1 can be used. Unfortunately, this method is not well suited to meet the challenges posed by the high dimension. In addition, it is necessary to know the number of clusters in order to use it. Chapter 2 presents three methods that we have developed to try to solve the problems described above. The works presented there have not been thoroughly researched for various reasons. The first method that could be called 'graphical lasso on Gaussian mixtures' consists in estimating the inverse matrices of covariance matrices $Sigma$ (Section 2.1) in the hypothesis that they are parsimonious. We adapt the graphic lasso method of [Friedman et al., 2007] to a component in the case of a mixture and experimentally evaluate this method. The other two methods address the problem of estimating the number of clusters in the mixture. The first is a penalized estimate of the matrix of posterior probabilities $ Tau in RR ^ {n times K} $ whose component $ (i, j) $ is the probability that the $i$-th observation is in the $j$-th cluster. Unfortunately, this method proved to be too expensive in complexity (Section 2.2.1). Finally, the second method considered is to penalize the weight vector $ pi $ in order to make it parsimonious. This method shows promising results (Section 2.2.2). In Chapter 3, we study the maximum likelihood estimator of density of $n$ i.i.d observations, under the assumption that it is well approximated by a mixture with a large number of components. The main focus is on statistical properties with respect to the Kullback-Leibler loss. We establish risk bounds taking the form of sharp oracle inequalities both in deviation and in expectation. A simple consequence of these bounds is that the maximum likelihood estimator attains the optimal rate $((log K)/n)^{1/2}$, up to a possible logarithmic correction, in the problem of convex aggregation when the number $K$ of components is larger than $n^{1/2}$. More importantly, under the additional assumption that the Gram matrix of the components satisfies the compatibility condition, the obtained oracle inequalities yield the optimal rate in the sparsity scenario. That is, if the weight vector is (nearly) $D$-sparse, we get the rate $(Dlog K)/n$. As a natural complement to our oracle inequalities, we introduce the notion of nearly-$D$-sparse aggregation and establish matching lower bounds for this type of aggregation. Finally, in Chapter 4, we propose an algorithm that performs the Kullback-Leibler aggregation of components of a dictionary as discussed in Chapter 3. We compare its performance with different methods: the kernel density estimator , the 'Adaptive Danzig' estimator, the SPADES and EM estimator with the BIC criterion. We then propose a method to build the dictionary of densities and study it numerically. This thesis was carried out within the framework of a CIFRE agreement with the company ARTEFACT
Gopalaswamy, Sundeep Lim Alvin S. "Dynamic clustering protocol based on relative speed in mobile ad hoc networks for intelligent vehicles." Auburn, Ala., 2007. http://repo.lib.auburn.edu/2007%20Fall%20Theses/GOPALASWAMY_SUNDEEP_4.pdf.
Повний текст джерелаYenket, Renoo. "Understanding methods for internal and external preference mapping and clustering in sensory analysis." Diss., Kansas State University, 2011. http://hdl.handle.net/2097/8770.
Повний текст джерелаDepartment of Human Nutrition
Edgar Chambers IV
Preference mapping is a method that provides product development directions for developers to see a whole picture of products, liking and relevant descriptors in a target market. Many statistical methods and commercial statistical software programs offering preference mapping analyses are available to researchers. Because of numerous available options, there are two questions addressed in this research that most scientists must answer before choosing a method of analysis: 1) are the different methods providing the same interpretation, co-ordinate values and object orientation; and 2) which method and program should be used with the data provided? This research used data from paint, milk and fragrance studies, representing complexity from lesser to higher. The techniques used are principal component analysis, multidimensional preference map (MDPREF), modified preference map (PREFMAP), canonical variate analysis, generalized procrustes analysis and partial least square regression utilizing statistical software programs of SAS, Unscrambler, Senstools and XLSTAT. Moreover, the homogeneousness of consumer data were investigated through hierarchical cluster analysis (McQuitty’s similarity analysis, median, single linkage, complete linkage, average linkage, and Ward’s method), partitional algorithm (k-means method), nonparametric method versus four manual clustering groups (strict, strict-liking-only, loose, loose-liking-only segments). The manual clusters were extracted according to the most frequently rated highest for best liked and least liked products on hedonic ratings. Furthermore, impacts of plotting preference maps for individual clusters were explored with and without the use of an overall mean liking vector. Results illustrated various statistical software programs were not similar in their oriented and co-ordinate values, even when using the same preference method. Also, if data were not highly homogenous, interpretation could be different. Most computer cluster analyses did not segment consumers relevant to their preferences and did not yield as homogenous clusters as manual clustering. The interpretation of preference maps created by the highest homogeneous clusters had little improvement when applied to complicated data. Researchers should look at key findings from univariate data in descriptive sensory studies to obtain accurate interpretations and suggestions from the maps, especially for external preference mapping. When researchers make recommendations based on an external map alone for complicated data, preference maps may be overused.
Ogden, Mitchell S. "Observing Clusters and Point Densities in Johnson City, TN Crime Using Nearest Neighbor Hierarchical Clustering and Kernel Density Estimation." Digital Commons @ East Tennessee State University, 2019. https://dc.etsu.edu/asrf/2019/schedule/138.
Повний текст джерелаInkaya, Tulin. "A Methodology Of Swarm Intelligence Application In Clustering Based On Neighborhood Construction." Phd thesis, METU, 2011. http://etd.lib.metu.edu.tr/upload/12613232/index.pdf.
Повний текст джерелаJones, Jesse Jack. "Effects of Non-homogeneous Population Distribution on Smoothed Maps Produced Using Kernel Density Estimation Methods." Thesis, University of North Texas, 2014. https://digital.library.unt.edu/ark:/67531/metadc699888/.
Повний текст джерелаOesterling, 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.
Повний текст джерелаSecchi, Alessandro. "Heterogeneous Effects of Monetary Policy." Doctoral thesis, Universitat Pompeu Fabra, 2005. http://hdl.handle.net/10803/7425.
Повний текст джерелаIn the third chapter we focus on a specific dimension along which the presence of heterogeneities in the balance sheet structure may induce different responses to a monetary policy action. In particular we address the existence of a channel of transmission of monetary policy, the cost-channel, that operates through the effect of interest expenses on the marginal cost of production. Such a channel is based on an active role of net working capital (inventories, plus trade receivables, less trade payables) in the production process and on the fact that variations in interest rate and credit conditions alter firms' short-run ability to produce final output by investing in net working capital. It has been argued that this mechanism may explain the dimension of the real effects of monetary policy, give a rationale for the positive short-run response of prices to rate increases (the "price puzzle") and call for a more gradual monetary policy response to shocks. The analysis is based on a unique panel, that includes about 2,000 Italian manufacturing firms and 14 years of data on individual prices and interest rates paid on several types of debt. We find robust evidence in favor of the presence of a cost-channel of monetary policy transmission, proportional to the amount of working capital held by each firm and with a size large enough to have non-trivial monetary policy implications.
The empirical analysis of chapter three is based on the hypothesis that the type of heterogeneity that produces different firm level responses to an interest rate variation is well defined and measurable. On the contrary, most of the empirical literature that tests for the existence of heterogeneous effects of monetary policy on firms' production or investment choices is based on an ad hoc assumption of the specific firm level characteristic that should distinguish more sensitive from less sensitive firms. A similar degree of arbitrariness is adopted in selecting the number of classes of firms characterized by different responses to monetary policy shocks as well as in the selection of the cutoff points. The objective of chapter four is to apply a recent econometric methodology that building on data predictive density provides a well defined criteria to detect both the "optimal" dimension along which analyze firms' responses to monetary policy innovations and the "optimal" endogenous groups. The empirical analysis is focused on Italian manufacturing firms and, in particular, on the response of inventory investment to monetary policy shocks from 1983 to 1998. The main results are the following. In strike contrast with what is normally assumed in the literature in most of the cases it turns out that the optimal number of classes that is larger than two. Moreover orderings that are based on variables that are normally thought to be equivalent proxies for the size of the firm (i.e. turnover, total assets and level of employment) do not lead neither to the same number of groups nor to similar splitting points. Finally even if endogenous clusters are mainly characterized by different degrees of within group heterogeneity, with groups composed by smaller firms showing the largest dispersion, there also exist important differences in the average effect of monetary policy across groups. In particular the fact that some of the orderings do not show the expected monotonicity between the rank and the average effect appears to be one of the most remarkable aspects.
Martinico, Bruno. "Applicazione di tecniche di clustering spaziale su dati macrosismici di felt reports per stimare i parametri dei terremoti." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2021. http://amslaurea.unibo.it/24993/.
Повний текст джерелаRuzgas, Tomas. "Daugiamačio pasiskirstymo tankio neparametrinis įvertinimas naudojant stebėjimų klasterizavimą." Doctoral thesis, Lithuanian Academic Libraries Network (LABT), 2007. http://vddb.library.lt/obj/LT-eLABa-0001:E.02~2007~D_20070314_094140-20878.
Повний текст джерелаGrierson, Greg Michael Jr. "Analysis of Amur honeysuckle Stem Density as a Function of Spatial Clustering, Horizontal Distance from Streams, Trails, and Elevation in Riparian Forests, Greene County, Ohio." Wright State University / OhioLINK, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=wright1621942350540022.
Повний текст джерелаMarević, Petar. "Towards a unified description of quantum liquid and cluster states in atomic nuclei within the relativistic energy density functional framework." Thesis, Université Paris-Saclay (ComUE), 2018. http://www.theses.fr/2018SACLS358/document.
Повний текст джерелаIn this thesis we develop a symmetry-conserving collective model for nuclear structure studies based on the relativistic energy density functional framework. Axially-symmetric quadrupole- and octupole-deformed reference states are generated by solving the relativistic Hartree-Bogoliubov equations. In the particle-hole channel of the effective interaction we employ the covariant point-coupling DD-PC1 functional, while the non-relativistic pairing force separable in momentum space is used in the particle-particle channel. Collective correlations related to restoration of broken symmetries are accounted for by simultaneously projecting reference states on good values of angular momenta, parity, and particle numbers. In the next step, symmetry-restored states are mixed within the generator coordinate method formalism. This enables us to obtain detailed spectroscopic predictions, including excitation energies, electromagnetic multipole moments and transition rates, as well as both the elastic and inelastic form factors. The described framework is global and it can be employed in various nuclear structure studies across the entire nuclide chart. As a first application, we will study formation of clusters in light nuclei. Nuclear clustering is considered to be a transitional phenomenon between quantum-liquid and solid phases in nuclei. In contrast to the conventional homogeneous quantum-liquid picture, spatial localization of alpha-particles gives rise to a molecule-like picture of atomic nuclei. In particular, we carry out a comprehensive analysis of quadrupole-octupole collectivity and cluster structures in neon isotopes. A special attention is paid to the case of self-conjugate ²⁰Ne isotope, where cluster structures are thought to form already in the ground state. Finally, we study the low-lying structure of ¹²C isotope. We focus on the structure of bands built on 0⁺ states that are known to manifest a rich variety of shapes, including the triangular configurations of the Hoyle band and 3-alpha linear chains in higher states
Schmidt, Eric. "Atomistic modelling of precipitation in Ni-base superalloys." Thesis, University of Cambridge, 2019. https://www.repository.cam.ac.uk/handle/1810/275131.
Повний текст джерелаSvoboda, Tomáš. "Implementace statistické metody KDE+." Master's thesis, Vysoké učení technické v Brně. Ústav soudního inženýrství, 2016. http://www.nusl.cz/ntk/nusl-241303.
Повний текст джерелаProvencher, David. "Imagerie de l'activité cérébrale : structure ou signal?" Thèse, Université de Sherbrooke, 2017. http://hdl.handle.net/11143/10472.
Повний текст джерелаAbstract : Imaging neural activity allows studying normal and pathological function of the human brain, while also being a useful tool for diagnosis and neurosurgery planning. Electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) are some of the most commonly used functional imaging modalities, both in research and clinic. Many aspects of cerebral structure can however influence the measured signals, so that they do not only reflect neural activity. Taking them into account is therefore of import to correctly interpret results, especially when comparing subjects displaying large differences in brain anatomy. In addition, maturation, aging as well as some pathologies are associated with changes in brain structure. This acts as a confounding factor when analysing longitudinal data or comparing target and control groups. Yet, our understanding of structure-signal relationships remains incomplete and very few studies take them into account. My Ph.D. project consisted in studying the impacts of cerebral structure on EEG and fMRI signals as well as exploring potential solutions to mitigate them. In that regard, I first studied the effect of age-related cortical thinning on event-related desynchronization (ERD) in EEG. Results allowed identifying a negative linear relationship between ERD and cortical thickness, enabling signal correction using regression. I then investigated how the presence of veins in a region impacts the blood-oxygen-level dependent (BOLD) response measured in fMRI following visual stimulation. This work showed that local venous density, which strongly varies across regions and subjects, correlates positively with the BOLD response amplitude and delay. Finally, I adapted a data clustering technique to improve the detection of activated cortical regions in fMRI. This method allows eschewing many problematic assumptions used in classical fMRI analyses, reducing the impacts of cerebral structure on results and establishing richer brain activity maps. Globally, this work contributes to further our understanding of structure-signal interactions in EEG and fMRI as well as to develop analysis methods that reduce their impact on data interpretation in terms of neural activity.
Mohamad, Ranim. "Relaxation de la contrainte dans les hétérostructures Al(Ga)InN/GaN pour applications électroniques : modélisation des propriétés physiques et rôle de l'indium dans la dégradation des couches épitaxiales." Thesis, Normandie, 2018. http://www.theses.fr/2018NORMC229/document.
Повний текст джерелаFor the fabrication of nitride-based power microwave transistors, the InAlN alloy is considered to be a better barrier than AlGaN thanks to the lattice match with GaN for an indium composition around 18%. Thus the two-dimensional electron gas (2DEG) is generated only by the spontaneous polarization at the AlInN/GaN heterointerface for a production of highest performance transistors. However, during its growth on GaN, its crystalline quality deteriorates with the thickness and V-defects are formed at the layer surface. To determine the sources of this behavior, we carried out a theoretical study by molecular dynamics and ab initio techniques to analyze the stability and the properties of alloys of nitride compounds, focusing particularly on InAlN. The analysis of the phase diagrams showed that this alloy has a wide zone of instability versus the indium composition and a different behavior with InGaN with amplified instability under high compressive strain. By determining the energetic stability of the nitrogen vacancy could be catalyst for forming clusters in this alloy. These InN clusters introduce deep donor levels inside the band gap. With regard to treading dislocations, our results show that they will also tend to capture indium atoms in their cores in order to minimize their energy. Thus, we have been able to provide a theoretical basis that show that the nitrogen vacancy participates in the spontaneous degradation of the AlInN layers and that the threading dislocations participate by attracting the indium atoms and thus reinforcing the separation of phase in their vicinity
Xu, Sanlin, and SanlinXu@yahoo com. "Mobility Metrics for Routing in MANETs." The Australian National University. Faculty of Engineering and Information Technology, 2007. http://thesis.anu.edu.au./public/adt-ANU20070621.212401.
Повний текст джерелаHlosta, Martin. "Modul pro shlukovou analýzu systému pro dolování z dat." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2010. http://www.nusl.cz/ntk/nusl-237158.
Повний текст джерелаFansi, Tchango Arsène. "Reconnaissance comportementale et suivi multi-cible dans des environnements partiellement observés." Thesis, Université de Lorraine, 2015. http://www.theses.fr/2015LORR0156/document.
Повний текст джерелаIn this thesis, we are interested in the problem of pedestrian behavioral tracking within a critical environment partially under sensory coverage. While most of the works found in the literature usually focus only on either the location of a pedestrian or the activity a pedestrian is undertaking, we stands in a general view and consider estimating both data simultaneously. The contributions presented in this document are organized in two parts. The first part focuses on the representation and the exploitation of the environmental context for serving the purpose of behavioral estimation. The state of the art shows few studies addressing this issue where graphical models with limited expressiveness capacity such as dynamic Bayesian networks are used for modeling prior environmental knowledge. We propose, instead, to rely on richer contextual models issued from autonomous agent-based behavioral simulators and we demonstrate the effectiveness of our approach through extensive experimental evaluations. The second part of the thesis addresses the general problem of pedestrians’ mutual influences, commonly known as targets’ interactions, on their respective behaviors during the tracking process. Under the assumption of the availability of a generic simulator (or a function) modeling the tracked targets' behaviors, we develop a yet scalable approach in which interactions are considered at low computational cost. The originality of the proposed approach resides on the introduction of density-based aggregated information, called "representatives’’, computed in such a way to guarantee the behavioral diversity for each target, and on which the filtering system relies for computing, in a finer way, behavioral estimations even in case of occlusions. We present the modeling choices, the resulting algorithms as well as a set of challenging scenarios on which the proposed approach is evaluated