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Dissertations / Theses on the topic 'Clustering Applications'

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Published: 3 June 2025

Last updated: 20 July 2025

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1

Wei, Wutao. "Model Based Clustering Algorithms with Applications." Thesis, Purdue University, 2018. http://pqdtopen.proquest.com/#viewpdf?dispub=10830711.

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<p> In machine learning predictive area, unsupervised learning will be applied when the labels of the data are unavailable, laborious to obtain or with limited proportion. Based on the special properties of data, we can build models by understanding the properties and making some reasonable assumptions. In this thesis, we will introduce three practical problems and discuss them in detail. This thesis produces 3 papers as follow: Wei, Wutao, et al. "A Non-parametric Hidden Markov Clustering Model with Applications to Time Varying User Activity Analysis." ICMLA2015 Wei, Wutao, et al. "Dynamic B

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2

Woo, Kam Tim. "Applications of clustering techniques on communication systems /." View abstract or full-text, 2004. http://library.ust.hk/cgi/db/thesis.pl?ELEC%202004%20WOO.

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3

Xiang, Chongyuan. "Private k-means clustering : algorithms and applications." Thesis, Massachusetts Institute of Technology, 2016. http://hdl.handle.net/1721.1/106394.

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Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2016.<br>This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.<br>Cataloged from student-submitted PDF version of thesis.<br>Includes bibliographical references (pages 77-80).<br>Today is a new era of big data. We contribute our personal data for the common good simply by using our smart phones, searching the web and doing online transactions. Researchers, companies and governments use the

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4

Xie, Qing Yan. "K-Centers Dynamic Clustering Algorithms and Applications." University of Cincinnati / OhioLINK, 2013. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1384427644.

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5

Francisci, Giacomo. "Local depth functions and applications to clustering." Doctoral thesis, Università degli studi di Trento, 2022. https://hdl.handle.net/11572/329413.

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Local depth functions (LDFs) are used for describing the local geometric features and mode(s) in multidimensional distributions. In this thesis, we undertake a rigorous systematic study of LDFs and establish several analytical and statistical properties. First, we show that, when the underlying probability distribution is absolutely continuous, scaled versions of LDFs (referred to as τ-approximation) converge, uniformly and in L^q, to the density, when τ converges to zero. Second, we establish that, as the sample size diverges to infinity the centered and scaled sample LDFs converge in distrib

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6

Truong, Duy Tin. "Non-Redundant Overlapping Clustering: Algorithms and Applications." Doctoral thesis, Università degli studi di Trento, 2013. https://hdl.handle.net/11572/368951.

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Given a dataset, traditional clustering algorithms often only provide a single partitioning or a single view of the dataset. On complex tasks, many different clusterings of a dataset exist, thus alternative clusterings which are of high quality and different from given trivial clusterings are asked to have complementary views. The task is therefore a clear multi-objective optimization problem. However, most approaches in the literature optimize these objectives sequentially (one after another one) or indirectly (by some heuristic combination). This can result in solutions which are not Pareto-

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7

Truong, Duy Tin. "Non-Redundant Overlapping Clustering: Algorithms and Applications." Doctoral thesis, University of Trento, 2013. http://eprints-phd.biblio.unitn.it/1130/1/phdThesis.pdf.

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Given a dataset, traditional clustering algorithms often only provide a single partitioning or a single view of the dataset. On complex tasks, many different clusterings of a dataset exist, thus alternative clusterings which are of high quality and different from given trivial clusterings are asked to have complementary views. The task is therefore a clear multi-objective optimization problem. However, most approaches in the literature optimize these objectives sequentially (one after another one) or indirectly (by some heuristic combination). This can result in solutions which are not Pareto-

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8

Boutalbi, Rafika. "Model-based tensor (co)-clustering and applications." Electronic Thesis or Diss., Université Paris Cité, 2020. https://wo.app.u-paris.fr/cgi-bin/WebObjects/TheseWeb.woa/wa/show?t=7172&f=55867.

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La classification non supervisée ou clustering suscite un grand intérêt dans la communauté d’apprentissage machine. Etant donné un ensemble d'objets décrits par un ensemble d'attributs, le clustering vise à partitionner l'ensemble des objets en classes homogènes. Le regroupement ou catégorisation de cet ensemble, est souvent nécessaire pour le traitement de données massives, devenu actuellement un axe de recherche prioritaire. A noter que lorsqu'on s'intéresse au clustering, nous faisons généralement référence au clustering de l'ensemble des objets. Depuis deux décennies, un intérêt est porté

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9

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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10

Guigourès, Romain. "Utilisation des modèles de co-clustering pour l'analyse exploratoire des données." Phd thesis, Université Panthéon-Sorbonne - Paris I, 2013. http://tel.archives-ouvertes.fr/tel-00935278.

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Le co-clustering est une technique de classification consistant à réaliser une partition simultanée des lignes et des colonnes d'une matrice de données. Parmi les approches existantes, MODL permet de traiter des données volumineuses et de réaliser une partition de plusieurs variables, continues ou nominales. Nous utilisons cette approche comme référence dans l'ensemble des travaux de la thèse et montrons la diversité des problèmes de data mining pouvant être traités, comme le partitionnement de graphes, de graphes temporels ou encore le clustering de courbes. L'approche MODL permet d'obtenir d

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11

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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12

Kotala, Pratap. "Usability Construct for Mobile Applications: A Clustering based Approach." Diss., North Dakota State University, 2015. https://hdl.handle.net/10365/27027.

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The growth of mobile applications that run on cell phones and other handheld devices has introduced a broad range of usability challenges that were not faced by the web and standalone PC environments. The current usability models for mobile applications are mostly based on the experience of the usability experts and users that were collected through surveys and field studies. Many usability researchers and practitioners have developed conceptual usability frameworks that utilize either different or overlapping usability attributes. Moreover, the usability frameworks in existence they are limit

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13

González, García Juan. "Application of clustering analysis and sequence analysis on the performance analysis of parallel applications." Doctoral thesis, Universitat Politècnica de Catalunya, 2013. http://hdl.handle.net/10803/128875.

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High Performance Computing and Supercomputing is the high end area of the computing science that studies and develops the most powerful computers available. Current supercomputers are extremely complex so are the applications that run on them. To take advantage of the huge amount of computing power available it is strictly necessary to maximize the knowledge we have about how these applications behave and perform. This is the mission of the (parallel) performance analysis. In general, performance analysis toolkits oUer a very simplistic manipulations of the performance data. First order stat

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14

梁德貞 and Tak-ching Leung. "Correspondence analysis and clustering with applications to site-species occurrence." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 1991. http://hub.hku.hk/bib/B31209889.

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15

Renard, Benjamin. "Online time-dependent clustering with applications to distributed social networks." Thesis, McGill University, 2014. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=121552.

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Cluster analysis can be defined as the study of algorithms and methods that aim at organizing data into meaningful groups in an unsupervised fashion. These algorithms are essential to understand specific sets of data and the relations between items inside these sets, and are used in many scientific fields (e.g., computer vision, biology, economics). If the data evolves over time, we need specific algorithms to represent the evolution of the clustering of the dataset over time. An additional complication arises when we do not have the entire dataset at once but instead receive elements one by o

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16

Anderson, Niall Hay. "Methods for the investigation of spatial clustering, with epidemiological applications." Thesis, University of Glasgow, 1992. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.364260.

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17

Santiago, 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.

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Modularity Density Maximization is a graph clustering problem which avoids the resolution limit degeneracy of the Modularity Maximization problem. This thesis aims at solving larger instances than current Modularity Density heuristics do, and show how close the obtained solutions are to the expected clustering. Three main contributions arise from this objective. The first one is about the theoretical contributions about properties of Modularity Density based prioritizers. The second one is the development of eight Modularity Density Maximization heuristics. Our heuristics are compared with opt

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18

Bozdag, Doruk. "Graph Coloring and Clustering Algorithms for Science and Engineering Applications." The Ohio State University, 2008. http://rave.ohiolink.edu/etdc/view?acc_num=osu1229459765.

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19

Hsieh, Ya-Ching. "Homals-clustering analysis and its applications in computational sequence analysis." Diss., Restricted to subscribing institutions, 2007. http://proquest.umi.com/pqdweb?did=1472131881&sid=1&Fmt=2&clientId=1564&RQT=309&VName=PQD.

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20

Leung, Tak-ching. "Correspondence analysis and clustering with applications to site-species occurrence /." [Hong Kong] : University of Hong Kong, 1991. http://sunzi.lib.hku.hk/hkuto/record.jsp?B13039519.

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21

Raßer, Günter. "Clustering Partition Models for Discrete Structures with Applications in Geographical Epidemiology." Diss., lmu, 2003. http://nbn-resolving.de/urn:nbn:de:bvb:19-12932.

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22

Springer, Tobias [Verfasser]. "Total Frame Potential and its Applications in Data Clustering / Tobias Springer." Aachen : Shaker, 2014. http://d-nb.info/1049382927/34.

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23

Elisha, Karl Justin Edward. "A K-seed genetic clustering algorithm with applications to cellular manufacturing." Thesis, University of Birmingham, 1997. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.364921.

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24

Ezeozue, Chidube Donald. "Large-scale consensus clustering and data ownership considerations for medical applications." Thesis, Massachusetts Institute of Technology, 2013. http://hdl.handle.net/1721.1/86273.

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Thesis: S.M. in Technology and Policy, Massachusetts Institute of Technology, Engineering Systems Division, Technology and Policy Program, 2013.<br>Thesis: S.M., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2013.<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references (pages 97-101).<br>An intersection of events has led to a massive increase in the amount of medical data being collected from patients inside and outside the hospital. These events include the development of new sensors, the continuous decrease in the co

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25

Petrosyan, Vahan. "Fast, Robust and Scalable Clustering Algorithms with Applications in Computer Vision." Licentiate thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-238512.

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In this thesis, we address a number of challenges in cluster analysis. We begin by investigating one of the oldest and most challenging problems: determining the number of clusters, k. For this problem, we propose a novel solution that, unlike previous techniques, delivers both the number of clusters and the clusters in one-shot (in contrast, conventional techniques run a given clustering algorithm several times for different values of k, and/or for several initialization with the same k). The second challenge we treat is the drawback, briefly mentioned above, of many conventional iterative cl

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26

Kayo, O. (Olga). "Locally linear embedding algorithm:extensions and applications." Doctoral thesis, University of Oulu, 2006. http://urn.fi/urn:isbn:9514280415.

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Abstract Raw data sets taken with various capturing devices are usually multidimensional and need to be preprocessed before applying subsequent operations, such as clustering, classification, outlier detection, noise filtering etc. One of the steps of data preprocessing is dimensionality reduction. It has been developed with an aim to reduce or eliminate information bearing secondary importance, and retain or highlight meaningful information while reducing the dimensionality of data. Since the nature of real-world data is often nonlinear, linear dimensionality reduction techniques, such as pr

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27

Weber, Matthias. "Structural Performance Comparison of Parallel Software Applications." Doctoral thesis, Saechsische Landesbibliothek- Staats- und Universitaetsbibliothek Dresden, 2016. http://nbn-resolving.de/urn:nbn:de:bsz:14-qucosa-216133.

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With rising complexity of high performance computing systems and their parallel software, performance analysis and optimization has become essential in the development of efficient applications. The comparison of performance data is a key operation required in performance analysis. An analyst may conduct different types of comparisons in order to understand the performance properties of an application. One use case is comparing performance data from multiple measurements. Typical examples for such comparisons are before/after comparisons when applying optimizations or changing code versions. B

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28

Qin, Li-Xuan. "The clustering of regression models method with applications in gene expression data /." Thesis, Connect to this title online; UW restricted, 2005. http://hdl.handle.net/1773/9591.

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29

Xu, Yaomin. "New Clustering and Feature Selection Procedures with Applications to Gene Microarray Data." Case Western Reserve University School of Graduate Studies / OhioLINK, 2008. http://rave.ohiolink.edu/etdc/view?acc_num=case1196144281.

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30

Streib, Kevin. "IMPROVED GRAPH-BASED CLUSTERING WITH APPLICATIONS IN COMPUTER VISION AND BEHAVIOR ANALYSIS." The Ohio State University, 2012. http://rave.ohiolink.edu/etdc/view?acc_num=osu1331063343.

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31

Pandey, Ravi Shanker. "Markov Model of Segmentation and Clustering: Applications in Deciphering Genomes and Metagenomes." Thesis, University of North Texas, 2017. https://digital.library.unt.edu/ark:/67531/metadc1011827/.

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Rapidly accumulating genomic data as a result of high-throughput sequencing has necessitated development of efficient computational methods to decode the biological information underlying these data. DNA composition varies across structurally or functionally different regions of a genome as well as those of distinct evolutionary origins. We adapted an integrative framework that combines a top-down, recursive segmentation algorithm with a bottom-up, agglomerative clustering algorithm to decipher compositionally distinct regions in genomes. The recursive segmentation procedure entails fragmentin

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32

Doe, Julien Albert. "Sensor Fusion Algorithm for Airborne Autonomous Vehicle Collision Avoidance Applications." DigitalCommons@CalPoly, 2018. https://digitalcommons.calpoly.edu/theses/2004.

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A critical ability of any aircraft is to be able to detect potential collisions with other airborne objects, and maneuver to avoid these collisions. This can be done by utilizing sensors on the aircraft to monitor the sky for collision threats. However, several problems face a system which aims to use multiple sensors for target tracking. The data collected from sensors needs to be clustered, fused, and otherwise processed such that the flight control system can make accurate decisions based on it. Raw sensor data, while filled with useful information, is tainted with inaccuracies due to limit

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33

Falk, Joachim [Verfasser]. "A Clustering-Based MPSoC Design Flow for Data Flow-Oriented Applications / Joachim Falk." München : Verlag Dr. Hut, 2015. http://d-nb.info/1075409497/34.

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34

PALMAS, FRANCESCO. "Versatility and Clinical Applications of Metabolomics: Tissues and Biofluids Investigation for Pathological Clustering." Doctoral thesis, Università degli Studi di Cagliari, 2018. http://hdl.handle.net/11584/255984.

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Metabolomics is the discipline that comprehensively and simultaneously profile the metabolome within a sample or organism. Investigating the downstream processes from gene expression and protein synthesis to the metabolic network, metabolomics may be of pivotal importance for the design of new screening methodology, for the identification of novel biomarkers and for the study of diseases at molecular and biochemical level. To this end, the versatility of metabolomic strategy was exploited for the investigation of diverse clinical conditions and different phenotypes regarding pregnancy, n

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35

Muoh, Chibuike. "Sparsification for Topic Modeling and Applications to Information Retrieval." Kent State University / OhioLINK, 2009. http://rave.ohiolink.edu/etdc/view?acc_num=kent1259206719.

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36

TESFAYE, YONATAN TARIKU. "Applications of a graph theoretic based clustering framework in computer vision and pattern recognition." Doctoral thesis, Università IUAV di Venezia, 2018. http://hdl.handle.net/11578/282321.

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37

Hamid, A. "Artificial immune systems for optimization, clustering and classification applications in chemical sciences and engineering." Thesis(M.Sc.), CSIR-National Chemical Laboratory, Pune, 2012. http://dspace.ncl.res.in:8080/xmlui/handle/20.500.12252/2110.

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38

CAO, BAOQIANG. "ON APPLICATIONS OF STATISTICAL LEARNING TO BIOPHYSICS." University of Cincinnati / OhioLINK, 2007. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1168577852.

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39

Ronhovde, Cicily J. "Biomedical applications of mesoporous silica particles." Diss., University of Iowa, 2017. https://ir.uiowa.edu/etd/5837.

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Mesoporous silica particles are of significant interest for biomedical applications due to their good general biocompatibility compared to other nanoparticle matrices such as quantum dots, high specific surface areas up to 1000 m2/g, and extreme synthetic tunability in terms of particle size, pore size and topology, core material, and surface functionalization. For one application, drug delivery, mesoporous silica nanoparticles (MSNs) of two pore structures, MCM-41 – parallel, hexagonally ordered pores approximately 3 nm in diameter – and wormhole (WO) – interconnected, disordered pores also a

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40

Sublime, Jérémie. "Contributions au clustering collaboratif et à ses potentielles applications en imagerie à très haute résolution." Thesis, Université Paris-Saclay (ComUE), 2016. http://www.theses.fr/2016SACLA005/document.

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Cette thèse présente plusieurs algorithmes développés dans le cadre du projet ANR COCLICO et contient deux axes principaux :Le premier axe concerne l'introduction d'un algorithme applicable aux images satellite à très haute résolution, qui est basé sur les champs aléatoires de Markov et qui apporte des notions sémantiques sur les clusters découverts. Cet algorithme est inspiré de l'algorithme Iterated conditional modes (ICM) et permet de faire un clustering sur des segments d'images pré-traitées. La méthode que nous proposons permet de gérer des voisinages irréguliers entre segments et d'obten

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41

Cominetti, Allende Ornella Cecilia. "DifFUZZY : a novel clustering algorithm for systems biology." Thesis, University of Oxford, 2012. http://ora.ox.ac.uk/objects/uuid:072d11e5-9bf1-4c47-9593-4cdb7327feaa.

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Current studies of the highly complex pathobiology and molecular signatures of human disease require the analysis of large sets of high-throughput data, from clinical to genetic expression experiments, containing a wide range of information types. A number of computational techniques are used to analyse such high-dimensional bioinformatics data. In this thesis we focus on the development of a novel soft clustering technique, DifFUZZY, a fuzzy clustering algorithm applicable to a larger class of problems than other soft clustering approaches. This method is better at handling datasets that cont

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42

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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Reuter, Timo [Verfasser]. "Event-based stream classification framework – a supervised clustering approach for social media applications / Timo Reuter." Bielefeld : Universitätsbibliothek Bielefeld, 2015. http://d-nb.info/1072224682/34.

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Wu, Jing. "Creating and using multiple case bases in large scale CBR applications, a clustering-based approach." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1999. http://www.collectionscanada.ca/obj/s4/f2/dsk2/ftp01/MQ37669.pdf.

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Lin, Wu-Ja, and 林武杰. "Clustering with Applications." Thesis, 1999. http://ndltd.ncl.edu.tw/handle/25629498219380128545.

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博士<br>國立交通大學<br>資訊科學系<br>87<br>In the dissertation, we proposed two clustering methods which generate cluster representatives. The first method handled the bisection problem, and the second method focused on minimizing the sum of square errors for multiple-class clustering. We designed the first method, i.e., the bisection method, in a way that the mean, variance, and average radius of the input data could be preserved. More specifically, by preserving these data distribution features, we derived some formulas which could be use

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Foszner, Paweł. "Bi-clustering - algorithms and applications." Rozprawa doktorska, 2014. https://repolis.bg.polsl.pl/dlibra/docmetadata?showContent=true&id=23916.

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Foszner, Paweł. "Bi-clustering - algorithms and applications." Rozprawa doktorska, 2014. https://delibra.bg.polsl.pl/dlibra/docmetadata?showContent=true&id=23916.

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48

Kashef, Rasha. "Cooperative Clustering Model and Its Applications." Thesis, 2008. http://hdl.handle.net/10012/4009.

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Data clustering plays an important role in many disciplines, including data mining, machine learning, bioinformatics, pattern recognition, and other fields, where there is a need to learn the inherent grouping structure of data in an unsupervised manner. There are many clustering approaches proposed in the literature with different quality/complexity tradeoffs. Each clustering algorithm works on its domain space with no optimum solution to all datasets of different properties, sizes, structures, and distributions. Challenges in data clustering include, identifying proper number of clusters, sc

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49

Guan, Yuqiang. "Large-scale clustering: algorithms and applications." Thesis, 2006. http://hdl.handle.net/2152/2497.

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Cho, Hyuk. "Co-clustering algorithms : extensions and applications." 2008. http://hdl.handle.net/2152/17809.

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Co-clustering is rather a recent paradigm for unsupervised data analysis, but it has become increasingly popular because of its potential to discover latent local patterns, otherwise unapparent by usual unsupervised algorithms such as k-means. Wide deployment of co-clustering, however, requires addressing a number of practical challenges such as data transformation, cluster initialization, scalability, and so on. Therefore, this thesis focuses on developing sophisticated co-clustering methodologies to maturity and its ultimate goal is to promote co-clustering as an invaluable and indispensable

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