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Dissertations / Theses on the topic 'Clustering spectral'

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Published: 25 May 2024

Last updated: 31 July 2025

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1

Shortreed, Susan. "Learning in spectral clustering /." Thesis, Connect to this title online; UW restricted, 2006. http://hdl.handle.net/1773/8977.

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Larson, Ellis, and Nelly Åkerblom. "Spectral clustering for Meteorology." Thesis, KTH, Skolan för teknikvetenskap (SCI), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-297760.

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Climate is a tremendously complex topic, affecting many aspects of human activity and constantly changing. Defining some structures and rules for how it works is thereof of the utmost importance even though it might only cover a small part of the complexity. Cluster analysis is a tool developed in data analysis that is able to categorize data into groups of similar type. In this paper data from the Swedish Meteorological and Hydrological Institute (SMHI) is clustered to find a partitioning. The cluster analysis used is called Spectral clustering which is a family of methods making use of the s

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Gaertler, Marco. "Clustering with spectral methods." [S.l. : s.n.], 2002. http://www.bsz-bw.de/cgi-bin/xvms.cgi?SWB10101213.

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Masum, Mohammad. "Vertex Weighted Spectral Clustering." Digital Commons @ East Tennessee State University, 2017. https://dc.etsu.edu/etd/3266.

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Spectral clustering is often used to partition a data set into a specified number of clusters. Both the unweighted and the vertex-weighted approaches use eigenvectors of the Laplacian matrix of a graph. Our focus is on using vertex-weighted methods to refine clustering of observations. An eigenvector corresponding with the second smallest eigenvalue of the Laplacian matrix of a graph is called a Fiedler vector. Coefficients of a Fiedler vector are used to partition vertices of a given graph into two clusters. A vertex of a graph is classified as unassociated if the Fiedler coefficient of the v

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Larsson, Johan, and Isak Ågren. "Numerical Methods for Spectral Clustering." Thesis, KTH, Skolan för teknikvetenskap (SCI), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-275701.

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The Aviation industry is important to the European economy and development, therefore a study of the sensitivity of the European flight network is interesting. If clusters exist within the network, that could indicate possible vulnerabilities or bottlenecks, since that would represent a group of airports poorly connected to other parts of the network. In this paper a cluster analysis using spectral clustering is performed with flight data from 34 different European countries. The report also looks at how to implement the spectral clustering algorithm for large data sets. After performing the s

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Rossi, Alfred Vincent III. "Temporal Clustering of Finite Metric Spaces and Spectral k-Clustering." The Ohio State University, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=osu1500033042082458.

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Darke, Felix, and Blomkvist Linus Below. "Categorization of songs using spectral clustering." Thesis, KTH, Skolan för teknikvetenskap (SCI), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-297763.

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A direct consequence of the world becoming more digital is that the amount of available data grows, which presents great opportunities for organizations, researchers and institutions alike.However, this places a huge demand on efficient and understandable algorithms for analyzing vast datasets. This project is centered around using one of these algorithms for identifying groups of songs in a public dataset released by Spotify in 2018. This problem is part of a larger problem class, where one wish to assign data into groups, without the preexisting knowledge of what makes the different groups s

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Marotta, Serena. "Alcuni metodi matriciali per lo Spectral Clustering." Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2017. http://amslaurea.unibo.it/14122/.

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L'obiettivo di questa tesi è analizzare nel dettaglio un insieme di tecniche di analisi dei dati, volte alla selezione e al raggruppamento di elementi omogenei, in modo che si possano facilmente interfacciare tra di loro e fornire un utilizzo più semplice per chi opera nel settore.È introdotta la trattazione dei principali metodi di clustering: linkage, k-medie e in particolare spectral clustering, argomento centrale della mia tesi.

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Alshammari, Mashaan. "Graph Filtering and Automatic Parameter Selection for Efficient Spectral Clustering." Thesis, University of Sydney, 2020. https://hdl.handle.net/2123/24091.

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Spectral clustering is usually used to detect non-convex clusters. Despite being an effective method to detect this type of clusters, spectral clustering has two deficiencies that made it less attractive for the pattern recognition community. First, the graph Laplacian has to pass through eigen-decomposition to find the embedding space. This has been proved to be a computationally expensive process when the number of points is large. Second, spectral clustering used parameters that highly influence its outcome. Tuning these parameters manually would be a tedious process when examining differen

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Azam, Nadia Farhanaz. "Spectral clustering: An explorative study of proximity measures." Thesis, University of Ottawa (Canada), 2009. http://hdl.handle.net/10393/28238.

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In cluster analysis, data are clustered into meaningful groups so that the objects in the same group are very similar, and the objects residing in two different groups are different from one another. One such cluster analysis algorithm is called the spectral clustering algorithm, which originated from the area of graph partitioning. The input, in this case, is a similarity matrix, constructed from the pair-wise similarity between data objects. The algorithm uses the eigenvalues and eigenvectors of a normalized similarity matrix to partition the data. The pair-wise similarity between the object

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11

Kong, Tian Fook. "Multilevel spectral clustering : graph partitions and image segmentation." Thesis, Massachusetts Institute of Technology, 2008. http://hdl.handle.net/1721.1/45275.

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Thesis (S.M.)--Massachusetts Institute of Technology, Computation for Design and Optimization Program, 2008.<br>Includes bibliographical references (p. 145-146).<br>While the spectral graph partitioning method gives high quality segmentation, segmenting large graphs by the spectral method is computationally expensive. Numerous multilevel graph partitioning algorithms are proposed to reduce the segmentation time for the spectral partition of large graphs. However, the greedy local refinement used in these multilevel schemes has the tendency of trapping the partition in poor local minima. In thi

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12

Aven, Matthew. "Daily Traffic Flow Pattern Recognition by Spectral Clustering." Scholarship @ Claremont, 2017. http://scholarship.claremont.edu/cmc_theses/1597.

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This paper explores the potential applications of existing spectral clustering algorithms to real life problems through experiments on existing road traffic data. The analysis begins with an overview of previous unsupervised machine learning techniques and constructs an effective spectral clustering algorithm that demonstrates the analytical power of the method. The paper focuses on the spectral embedding method’s ability to project non-linearly separable, high dimensional data into a more manageable space that allows for accurate clustering. The key step in this method involves solving a norm

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13

Gan, Sonny. "The application of spectral clustering in drug discovery." Thesis, University of Sheffield, 2013. http://etheses.whiterose.ac.uk/4839/.

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The application of clustering algorithms to chemical datasets is well established and has been reviewed extensively. Recently, a number of ‘modern’ clustering algorithms have been reported in other fields. One example is spectral clustering, which has yielded promising results in areas such as protein library analysis. The term spectral clustering is used to describe any clustering algorithm that utilises the eigenpairs of a matrix as the basis for partitioning a dataset. This thesis describes the development and optimisation of a non-overlapping spectral clustering method that is based upon a

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Ghafoory, Jones. "p-Laplacian Spectral Clustering Applied in Software Testing." Thesis, KTH, Numerisk analys, NA, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-260255.

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Software testing plays a vital role in the software development life cycle. Having a more accurate and cost-efficient testing process is still demanded in the industry. Thus, test optimization becomes an important topic in both state of the art and state of the practice. Software testing today can be performed manually, automatically or semi-automatically. A manual test procedure is still popular for testing for instance in safety critical systems. For testing a software product manually, we need to create a set of manual test case specifications. The number of required test cases for testing

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Casaca, Wallace Correa de Oliveira. "Graph Laplacian for spectral clustering and seeded image segmentation." Universidade de São Paulo, 2014. http://www.teses.usp.br/teses/disponiveis/55/55134/tde-24062015-112215/.

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Image segmentation is an essential tool to enhance the ability of computer systems to efficiently perform elementary cognitive tasks such as detection, recognition and tracking. In this thesis we concentrate on the investigation of two fundamental topics in the context of image segmentation: spectral clustering and seeded image segmentation. We introduce two new algorithms for those topics that, in summary, rely on Laplacian-based operators, spectral graph theory, and minimization of energy functionals. The effectiveness of both segmentation algorithms is verified by visually evaluating the re

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Barreira, Daniel, and Netterström Nazar Maksymchuk. "Recommend Songs With Data From Spotify Using Spectral Clustering." Thesis, KTH, Skolan för teknikvetenskap (SCI), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-297683.

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Spotify, which is one of the worlds biggest music services, posted a data set and an open-ended challenge for music recommendation research. This study's goal is to recommend songs to playlists with the given data set from Spotify using Spectral clustering. While the given data set had 1 000 000 playlists, Spectral clustering was performed on a subset with 16 000 playlists due to the lack of computational resources. With four different weighting methods describing the connection between playlists, the study shows results of reasonable clusters where similar category of playlists were clustered

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Pihlström, Ralf. "On some Spectral Properties of Stochastic Similarity Matrices for Data Clustering." Thesis, Uppsala universitet, Tillämpad matematik och statistik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-396646.

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Carozza, Marina. "Matrici Laplaciane sui grafi, proprietà di interlacing ed applicazione allo spectral clustering." Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2019. http://amslaurea.unibo.it/18789/.

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L'obiettivo di questo elaborato è di esporre alcuni principali teoremi riguardanti proprietà spettrali di particolari matrici usate per descrivere dati, dette matrici Laplaciane, costruite a partire da grafi, e la loro applicazione nello spectral clustering. In particolare, viene analizzata una proprietà detta "proprietà di interlacing". L'ultimo capitolo sarà dedicato ad esperimenti numerici, volti ad illustrare computazionalmente i risultati teorici.

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Karatzoglou, Alexandros, and Ingo Feinerer. "Text Clustering with String Kernels in R." Department of Statistics and Mathematics, WU Vienna University of Economics and Business, 2006. http://epub.wu.ac.at/1002/1/document.pdf.

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We present a package which provides a general framework, including tools and algorithms, for text mining in R using the S4 class system. Using this package and the kernlab R package we explore the use of kernel methods for clustering (e.g., kernel k-means and spectral clustering) on a set of text documents, using string kernels. We compare these methods to a more traditional clustering technique like k-means on a bag of word representation of the text and evaluate the viability of kernel-based methods as a text clustering technique. (author's abstract)<br>Series: Research Report Series / Depar

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20

Mayer-Jochimsen, Morgan. "Clustering Methods and Their Applications to Adolescent Healthcare Data." Scholarship @ Claremont, 2013. http://scholarship.claremont.edu/scripps_theses/297.

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Clustering is a mathematical method of data analysis which identifies trends in data by efficiently separating data into a specified number of clusters so is incredibly useful and widely applicable for questions of interrelatedness of data. Two methods of clustering are considered here. K-means clustering defines clusters in relation to the centroid, or center, of a cluster. Spectral clustering establishes connections between all of the data points to be clustered, then eliminates those connections that link dissimilar points. This is represented as an eigenvector problem where the solution is

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Cresswell, Kellen Garrison. "Spectral methods for the detection and characterization of Topologically Associated Domains." VCU Scholars Compass, 2019. https://scholarscompass.vcu.edu/etd/6100.

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The three-dimensional (3D) structure of the genome plays a crucial role in gene expression regulation. Chromatin conformation capture technologies (Hi-C) have revealed that the genome is organized in a hierarchy of topologically associated domains (TADs), sub-TADs, and chromatin loops which is relatively stable across cell-lines and even across species. These TADs dynamically reorganize during development of disease, and exhibit cell- and conditionspecific differences. Identifying such hierarchical structures and how they change between conditions is a critical step in understanding genome reg

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22

Blakely, Logan. "Spectral Clustering for Electrical Phase Identification Using Advanced Metering Infrastructure Voltage Time Series." Thesis, Portland State University, 2019. http://pqdtopen.proquest.com/#viewpdf?dispub=10980011.

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<p> The increasing demand for and prevalence of distributed energy resources (DER) such as solar power, electric vehicles, and energy storage, present a unique set of challenges for integration into a legacy power grid, and accurate models of the low-voltage distribution systems are critical for accurate simulations of DER. Accurate labeling of the phase connections for each customer in a utility model is one area of grid topology that is known to have errors and has implications for the safety, efficiency, and hosting capacity of a distribution system. This research presents a methodology for

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23

Reizer, Gabriella v. "Stability Selection of the Number of Clusters." Digital Archive @ GSU, 2011. http://digitalarchive.gsu.edu/math_theses/98.

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Selecting the number of clusters is one of the greatest challenges in clustering analysis. In this thesis, we propose a variety of stability selection criteria based on cross validation for determining the number of clusters. Clustering stability measures the agreement of clusterings obtained by applying the same clustering algorithm on multiple independent and identically distributed samples. We propose to measure the clustering stability by the correlation between two clustering functions. These criteria are motivated by the concept of clustering instability proposed by Wang (2010), which is

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Passmoor, Sean Stuart. "Clustering studies of radio-selected galaxies." Thesis, University of the Western Cape, 2011. http://etd.uwc.ac.za/index.php?module=etd&action=viewtitle&id=gen8Srv25Nme4_7521_1332410859.

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<p>We investigate the clustering of HI-selected galaxies in the ALFALFA survey and compare results with those obtained for HIPASS. Measurements of the angular correlation function and the inferred 3D-clustering are compared with results from direct spatial-correlation measurements. We are able to measure clustering on smaller angular scales and for galaxies with lower HI masses than was previously possible. We calculate the expected clustering of dark matter using the redshift distributions of HIPASS and ALFALFA and show that the ALFALFA sample is somewhat more anti-biased with respect to dark

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Furuhashi, Takeshi, Tomohiro Yoshikawa, and Kazuto Inagaki. "A Study on Extraction of Minority Groups in Questionnaire Data based on Spectral Clustering." IEEE, 2014. http://hdl.handle.net/2237/20713.

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26

Bellam, Venkata Pavan Kumar. "Efficient Community Detection for Large Scale Networks via Sub-sampling." Thesis, Virginia Tech, 2018. http://hdl.handle.net/10919/81862.

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Many real-world systems can be represented as network-graphs. Some of the networks have an inherent community structure based on interactions. The problem of identifying this grouping structure given a graph is termed as community detection problem which has certain existing algorithms. This thesis contributes by providing specific improvements to various community detection algorithms such as spectral clustering and extreme point algorithm. One of the main contributions is proposing a new sub-sampling method to make existing spectral clustering method scalable by reducing the computational co

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Felizardo, Rui Miguel Meireles. "A study on parallel versus sequential relational fuzzy clustering methods." Master's thesis, Faculdade de Ciências e Tecnologia, 2011. http://hdl.handle.net/10362/5663.

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Dissertação para obtenção do Grau de Mestre em Engenharia Informática<br>Relational Fuzzy Clustering is a recent growing area of study. New algorithms have been developed,as FastMap Fuzzy c-Means (FMFCM) and the Fuzzy Additive Spectral Clustering Method(FADDIS), for which it had been obtained interesting experimental results in the corresponding founding works. Since these algorithms are new in the context of the Fuzzy Relational clustering community, not many experimental studies are available. This thesis comes in response to the need of further investigation on these algorithms, concerning

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Stephani, Henrike [Verfasser], Gabriele [Akademischer Betreuer] Steidl, and Erich Peter [Akademischer Betreuer] Klement. "Automatic Segmentation and Clustering of Spectral Terahertz Data / Henrike Stephani. Betreuer: Gabriele Steidl ; Erich Peter Klement." Kaiserslautern : Technische Universität Kaiserslautern, 2012. http://d-nb.info/1027625681/34.

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Stephani, Henrike [Verfasser], Gabriele Akademischer Betreuer] Steidl, and Erich Peter [Akademischer Betreuer] [Klement. "Automatic Segmentation and Clustering of Spectral Terahertz Data / Henrike Stephani. Betreuer: Gabriele Steidl ; Erich Peter Klement." Kaiserslautern : Technische Universität Kaiserslautern, 2012. http://nbn-resolving.de/urn:nbn:de:hbz:386-kluedo-31630.

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Amaduzzi, Alberto. "Enzymes' characterization via spectral analysis of the Laplacian associated to their relative contact maps." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2021. http://amslaurea.unibo.it/23899/.

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The main motivation for my thesis is the believe that global properties of enzymes areessential for a complete understanding of their behaviors. In my thesis, in particular,I investigate qualitative properties of enzymes via spectral techniques associated to thegraph Laplacian. I try to apply visualization techniques to understand similarities anddissimilarities among different enzymes’ structures, encoded in adjacency matrices re-trieved from coordinate data in online available datasets. The purpose is to make anexploration of features and see whether these techniques, that are

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Bezek, Perit. "A Clustering Method For The Problem Of Protein Subcellular Localization." Master's thesis, METU, 2006. http://etd.lib.metu.edu.tr/upload/12607981/index.pdf.

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In this study, the focus is on predicting the subcellular localization of a protein, since subcellular localization is helpful in understanding a protein&rsquo<br>s functions. Function of a protein may be estimated from its sequence. Motifs or conserved subsequences are strong indicators of function. In a given sample set of protein sequences known to perform the same function, a certain subsequence or group of subsequences should be common<br>that is, occurrence (frequency) of common subsequences should be high. Our idea is to find the common subsequences through clustering and use these comm

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Curado, Manuel. "Structural Similarity: Applications to Object Recognition and Clustering." Doctoral thesis, Universidad de Alicante, 2018. http://hdl.handle.net/10045/98110.

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In this thesis, we propose many developments in the context of Structural Similarity. We address both node (local) similarity and graph (global) similarity. Concerning node similarity, we focus on improving the diffusive process leading to compute this similarity (e.g. Commute Times) by means of modifying or rewiring the structure of the graph (Graph Densification), although some advances in Laplacian-based ranking are also included in this document. Graph Densification is a particular case of what we call graph rewiring, i.e. a novel field (similar to image processing) where input graphs are

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Brocklebank, Sean. "Inquiry into the nature and causes of individual differences in economics." Thesis, University of Edinburgh, 2012. http://hdl.handle.net/1842/6281.

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The thesis contains four chapters on the structure and predictability of individual differences Chapter 1. Re-analyses data from Holt and Laury's (2002) risk aversion experiments. Shows that big-stakes hypothetical payoffs are better than small-stakes real-money payoffs for predicting choices in big-stakes real-money gambles (in spite of the presence of hypothetical bias). Argues that hypothetical bias is a problem for calibration of mean preferences but not for prediction of the rank order of subjects' preferences. Chapter 2. Describes an experiment: Participants were given personality tests

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Fairbanks, James Paul. "Graph analysis combining numerical, statistical, and streaming techniques." Diss., Georgia Institute of Technology, 2016. http://hdl.handle.net/1853/54972.

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Graph analysis uses graph data collected on a physical, biological, or social phenomena to shed light on the underlying dynamics and behavior of the agents in that system. Many fields contribute to this topic including graph theory, algorithms, statistics, machine learning, and linear algebra. This dissertation advances a novel framework for dynamic graph analysis that combines numerical, statistical, and streaming algorithms to provide deep understanding into evolving networks. For example, one can be interested in the changing influence structure over time. These disparate techniques each co

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He, Guanlin. "Parallel algorithms for clustering large datasets on CPU-GPU heterogeneous architectures." Electronic Thesis or Diss., université Paris-Saclay, 2022. http://www.theses.fr/2022UPASG062.

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Clustering, qui consiste à réaliser des groupements naturels de données, est une tâche fondamentale et difficile dans l'apprentissage automatique et l'exploration de données. De nombreuses méthodes de clustering ont été proposées dans le passé, parmi lesquelles le clustering en k-moyennes qui est une méthode couramment utilisée en raison de sa simplicité et de sa rapidité.Le clustering spectral est une approche plus récente qui permet généralement d'obtenir une meilleure qualité de clustering que les k-moyennes. Cependant, les algorithmes classiques de clustering spectral souffrent d'un manque

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Lee, Zed Heeje. "A graph representation of event intervals for efficient clustering and classification." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-281947.

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Sequences of event intervals occur in several application domains, while their inherent complexity hinders scalable solutions to tasks such as clustering and classification. In this thesis, we propose a novel spectral embedding representation of event interval sequences that relies on bipartite graphs. More concretely, each event interval sequence is represented by a bipartite graph by following three main steps: (1) creating a hash table that can quickly convert a collection of event interval sequences into a bipartite graph representation, (2) creating and regularizing a bi-adjacency matrix

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Gustavsson, Hanna. "Clustering Based Outlier Detection for Improved Situation Awareness within Air Traffic Control." Thesis, KTH, Optimeringslära och systemteori, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-264215.

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The aim of this thesis is to examine clustering based outlier detection algorithms on their ability to detect abnormal events in flight traffic. A nominal model is trained on a data-set containing only flights which are labeled as normal. A detection scoring function based on the nominal model is used to decide if a new and in forehand unseen data-point behaves like the nominal model or not. Due to the unknown structure of the data-set three different clustering algorithms are examined for training the nominal model, K-means, Gaussian Mixture Model and Spectral Clustering. Depending on the nom

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Miti, Filippo. "Mathematical models for cellular aggregation: the chemotactic instability and clustering formation." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2016. http://amslaurea.unibo.it/12020/.

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In this thesis we present a mathematical formulation of the interaction between microorganisms such as bacteria or amoebae and chemicals, often produced by the organisms themselves. This interaction is called chemotaxis and leads to cellular aggregation. We derive some models to describe chemotaxis. The first is the pioneristic Keller-Segel parabolic-parabolic model and it is derived by two different frameworks: a macroscopic perspective and a microscopic perspective, in which we start with a stochastic differential equation and we perform a mean-field approximation. This parabolic model may b

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Henley, Lisa. "The quantification and visualisation of human flourishing." Thesis, University of Canterbury. School of Mathematics and Statistics, 2015. http://hdl.handle.net/10092/10441.

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Economic indicators such as GDP have been a main indicator of human progress since the first half of last century. There is concern that continuing to measure our progress and / or wellbeing using measures that encourage consumption on a planet with limited resources, may not be ideal. Alternative measures of human progress, have a top down approach where the creators decide what the measure will contain. This work defines a 'bottom up' methodology an example of measuring human progress that doesn't require manual data reduction. The technique allows visual overlay of other 'factors' that us

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Storer, Jeremy J. "Computational Intelligence and Data Mining Techniques Using the Fire Data Set." Bowling Green State University / OhioLINK, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=bgsu1460129796.

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Schuetter, Jared Michael. "Cairn Detection in Southern Arabia Using a Supervised Automatic Detection Algorithm and Multiple Sample Data Spectroscopic Clustering." The Ohio State University, 2010. http://rave.ohiolink.edu/etdc/view?acc_num=osu1269567071.

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Benigni, Matthew Curran. "Detection and Analysis of Online Extremist Communities." Research Showcase @ CMU, 2017. http://repository.cmu.edu/dissertations/949.

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Online social networks have become a powerful venue for political activism. In many cases large, insular online communities form that have been shown to be powerful diffusion mechanisms of both misinformation and propaganda. In some cases these groups users advocate actions or policies that could be construed as extreme along nearly any distribution of opinion, and are thus called Online Extremist Communities (OECs). Although these communities appear increasingly common, little is known about how these groups form or the methods used to influence them. The work in this thesis provides research

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Fender, Alexandre. "Solutions parallèles pour les grands problèmes de valeurs propres issus de l'analyse de graphe." Thesis, Université Paris-Saclay (ComUE), 2017. http://www.theses.fr/2017SACLV069/document.

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Les graphes, ou réseaux, sont des structures mathématiques représentant des relations entre des éléments. Ces systèmes peuvent être analysés dans le but d’extraire des informations sur la structure globale ou sur des composants individuels. L'analyse de graphe conduit souvent à des problèmes hautement complexes à résoudre. À grande échelle, le coût de calcul de la solution exacte est prohibitif. Heureusement, il est possible d’utiliser des méthodes d’approximations itératives pour parvenir à des estimations précises. Lesméthodes historiques adaptées à un petit nombre de variables ne conviennen

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Mandrell, Christopher. "IMPROVING SPECTRAL ANALYSIS WITH THE APPLICATION OF MACHINE LEARNING: STUDY OF LASER-INDUCED BREAKDOWN SPECTROSCOPY (LIBS) AND RAMAN SPECTROSCOPY WITH CLASSIFICATION AND CLUSTERING TECHNIQUES." OpenSIUC, 2020. https://opensiuc.lib.siu.edu/theses/2665.

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AN ABSTRACT OF THE THESIS OFChristopher T. Mandrell, for the Master of Science degree in Physics, presented on April 8, 2020, at Southern Illinois University Carbondale.TITLE: IMPROVING SPECTRAL ANALYSIS WITH THE APPLICATION OF MACHINE LEARNING: STUDY OF LASER-INDUCED BREAKDOWN SPECTROSCOPY (LIBS) AND RAMAN SPECTROSCOPY WITH CLASSIFICATION AND CLUSTERING TECHNIQUESMAJOR PROFESSOR: Dr. Poopalasingam SivakumarAtomic and molecular spectroscopy, in the form of LIBS emissions and Raman scattering, respectively, are tools that provide a vast amount of information with little to no sample preparation

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Mouysset, Sandrine. "Contributions à l'étude de la classification spectrale et applications." Phd thesis, Institut National Polytechnique de Toulouse - INPT, 2010. http://tel.archives-ouvertes.fr/tel-00573433.

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La classification spectrale consiste à créer, à partir des éléments spectraux d'une matrice d'affinité gaussienne, un espace de dimension réduite dans lequel les données sont regroupées en classes. Cette méthode non supervisée est principalement basée sur la mesure d'affinité gaussienne, son paramètre et ses éléments spectraux. Cependant, les questions sur la séparabilité des classes dans l'espace de projection spectral et sur le choix du paramètre restent ouvertes. Dans un premier temps, le rôle du paramètre de l'affinité gaussienne sera étudié à travers des mesures de qualités et deux heuris

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Westerlund, Annie M. "Computational Study of Calmodulin’s Ca2+-dependent Conformational Ensembles." Licentiate thesis, KTH, Biofysik, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-234888.

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Ca2+ and calmodulin play important roles in many physiologically crucial pathways. The conformational landscape of calmodulin is intriguing. Conformational changes allow for binding target-proteins, while binding Ca2+ yields population shifts within the landscape. Thus, target-proteins become Ca2+-sensitive upon calmodulin binding. Calmodulin regulates more than 300 target-proteins, and mutations are linked to lethal disorders. The mechanisms underlying Ca2+ and target-protein binding are complex and pose interesting questions. Such questions are typically addressed with experiments which fail

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Nardoni, Chiara. "Diffusion maps in the Subriemannian motion group and perceptual grouping." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2014. http://amslaurea.unibo.it/6971/.

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Il presente lavoro è motivato dal problema della constituzione di unità percettive a livello della corteccia visiva primaria V1. Si studia dettagliatamente il modello geometrico di Citti-Sarti con particolare attenzione alla modellazione di fenomeni di associazione visiva. Viene studiato nel dettaglio un modello di connettività. Il contributo originale risiede nell'adattamento del metodo delle diffusion maps, recentemente introdotto da Coifman e Lafon, alla geometria subriemanniana della corteccia visiva. Vengono utilizzati strumenti di teoria del potenziale, teoria spettrale, analisi armonic

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Witt, Walter G. "Quantifying the Structure of Misfolded Proteins Using Graph Theory." Digital Commons @ East Tennessee State University, 2017. https://dc.etsu.edu/etd/3244.

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The structure of a protein molecule is highly correlated to its function. Some diseases such as cystic fibrosis are the result of a change in the structure of a protein so that this change interferes or inhibits its function. Often these changes in structure are caused by a misfolding of the protein molecule. To assist computational biologists, there is a database of proteins together with their misfolded versions, called decoys, that can be used to test the accuracy of protein structure prediction algorithms. In our work we use a nested graph model to quantify a selected set of proteins that

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Saade, Alaa. "Spectral inference methods on sparse graphs : theory and applications." Thesis, Paris Sciences et Lettres (ComUE), 2016. http://www.theses.fr/2016PSLEE024/document.

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Face au déluge actuel de données principalement non structurées, les graphes ont démontré, dans une variété de domaines scientifiques, leur importance croissante comme language abstrait pour décrire des interactions complexes entre des objets complexes. L’un des principaux défis posés par l’étude de ces réseaux est l’inférence de propriétés macroscopiques à grande échelle, affectant un grand nombre d’objets ou d’agents, sur la seule base des interactions microscopiquesqu’entretiennent leurs constituants élémentaires. La physique statistique, créée précisément dans le but d’obtenir les lois mac

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Voiron, Nicolas. "Structuration de bases multimédia pour une exploration visuelle." Thesis, Université Grenoble Alpes (ComUE), 2015. http://www.theses.fr/2015GREAA036/document.

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La forte augmentation du volume de données multimédia impose la mise au point de solutions adaptées pour une exploration visuelle efficace des bases multimédia. Après avoir examiné les processus de visualisation mis en jeu, nous remarquons que ceci demande une structuration des données. L’objectif principal de cette thèse est de proposer et d’étudier ces méthodes de structuration des bases multimédia en vue de leur exploration visuelle.Nous commençons par un état de l’art détaillant les données et les mesures que nous pouvons produire en fonction de la nature des variables décrivant les donnée

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