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Thèses sur le sujet « Fuzyy C-Means »

Auteur : Grafiati

Publié le 7 juin 2025

Mis à jour le 2 août 2025

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1

Dagher, Issam J. "Gradient based fuzzy c-means algorithm." FIU Digital Commons, 1994. http://digitalcommons.fiu.edu/etd/2652.

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A clustering algorithm based on the Fuzzy c-means algorithm (FCM) and the gradient descent method is presented. In the FCM, the minimization process of the objective function is proceeded by solving two equations alternatively in an iterative fashion. Each iteration requires the use of all the data at once. In our proposed approach one datum is presented at a time to the network and the minimization is proceeded using the gradient descent method. Compared to FCM, the experimental results show that our algorithm is very competitive in terms of speed and stability of convergence for large number

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Hong, Sui. "Experiments with K-Means, Fuzzy c-Means and Approaches to Choose K and C." Honors in the Major Thesis, University of Central Florida, 2006. http://digital.library.ucf.edu/cdm/ref/collection/ETH/id/1224.

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This item is only available in print in the UCF Libraries. If this is your Honors Thesis, you can help us make it available online for use by researchers around the world by following the instructions on the distribution consent form at http://library.ucf<br>Bachelors<br>Engineering and Computer Science<br>Computer Engineering

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GOMES, Evanice Pinheiro. "Regionalização de precipitações via fuzzy C-means." Universidade Federal do Pará, 2017. http://repositorio.ufpa.br/jspui/handle/2011/9833.

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Camara, Assa. "Využití fuzzy množin ve shlukové analýze se zaměřením na metodu Fuzzy C-means Clustering." Master's thesis, Vysoké učení technické v Brně. Fakulta strojního inženýrství, 2020. http://www.nusl.cz/ntk/nusl-417051.

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This master thesis deals with cluster analysis, more specifically with clustering methods that use fuzzy sets. Basic clustering algorithms and necessary multivariate transformations are described in the first chapter. In the practical part, which is in the third chapter we apply fuzzy c-means clustering and k-means clustering on real data. Data used for clustering are the inputs of chemical transport model CMAQ. Model CMAQ is used to approximate concentration of air pollutants in the atmosphere. To the data we will apply two different clustering methods. We have used two different methods to s

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Khmag, Abdulhakim Emhemad. "Fuzzy land cover change detection and validation : a comparison of fuzzy and Boolean analyses in Tripoli City, Libya." Thesis, University of Leicester, 2013. http://hdl.handle.net/2381/27811.

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This research extends fuzzy methods to consider the fuzzy validation of fuzzy land cover data at the sub-pixel level. The study analyses the relationships between fuzzy memberships generated by field survey and those generated from the classification of remotely sensed data. In so doing it examines the variations in the relationship between observed and predicted fuzzy land cover classes. This research applies three land cover classification techniques: Fuzzy sets, Fuzzy c-means and Boolean classification, and develops three models to determine fuzzy land cover change. The first model is depen

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Schenatto, Kelyn. "Utilização de métodos de interpolação e agrupamento para definição de unidades de manejo em agricultura de precisão." Universidade Estadual do Oeste do Parana, 2014. http://tede.unioeste.br:8080/tede/handle/tede/178.

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Made available in DSpace on 2017-05-12T14:46:57Z (GMT). No. of bitstreams: 1 Kelyn Schenatto.pdf: 4212903 bytes, checksum: 0ba04350cc25aff5e6acb249938e5375 (MD5) Previous issue date: 2014-02-04<br>Despite the benefits offered by the technology of precision agriculture (PA), the necessity of dense sampling grids and use of sophisticated equipment for the soil and plant handling make it financially unfeasible in many cases, especially for small producers. With the aimof making viable the PA, the definition of management zones (MZ) consists in dividing the plotin subregions that have similar ph

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Milagre, Selma Terezinha. "Análise do número de grupos em bases de dados incompletas utilizando agrupamentos nebulosos e reamostragem Bootstrap." Universidade de São Paulo, 2008. http://www.teses.usp.br/teses/disponiveis/18/18153/tde-04032009-150315/.

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A técnica de agrupamento de dados é amplamente utilizada em análise exploratória, a qual é frequentemente necessária em diversas áreas de pesquisa tais como medicina, biologia e estatística, para avaliar potenciais hipóteses a serem utilizadas em estudos subseqüentes. Em bases de dados reais, a ocorrência de dados incompletos, nos quais os valores de um ou mais atributos do dado são desconhecidos, é bastante comum. Este trabalho apresenta um método capaz de identificar o número de grupos presentes em bases de dados incompletas, utilizando a combinação das técnicas de agrupamentos nebulosos e r

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FURUHASHI, Takeshi, and Makoto YASUDA. "Fuzzy Entropy Based Fuzzy c-Means Clustering with Deterministic and Simulated Annealing Methods." Institute of Electronics, Information and Communication Engineers, 2009. http://hdl.handle.net/2237/15060.

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PIMENTEL, Bruno Almeida. "Métodos de agrupamento difuso multivariado baseados no Fuzzy C-means." Universidade Federal de Pernambuco, 2017. https://repositorio.ufpe.br/handle/123456789/27506.

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Submitted by Fernanda Rodrigues de Lima (fernanda.rlima@ufpe.br) on 2018-10-05T21:21:36Z No. of bitstreams: 2 license_rdf: 811 bytes, checksum: e39d27027a6cc9cb039ad269a5db8e34 (MD5) TESE Bruno Almeida Pimentel.pdf: 1823660 bytes, checksum: 2dda6d26c679f00a5e642b456730f356 (MD5)<br>Approved for entry into archive by Alice Araujo (alice.caraujo@ufpe.br) on 2018-11-14T20:59:03Z (GMT) No. of bitstreams: 2 license_rdf: 811 bytes, checksum: e39d27027a6cc9cb039ad269a5db8e34 (MD5) TESE Bruno Almeida Pimentel.pdf: 1823660 bytes, checksum: 2dda6d26c679f00a5e642b456730f356 (MD5)<br>Made available in DSp

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Naik, Vaibhav C. "Fuzzy C-means clustering approach to design a warehouse layout." [Tampa, Fla.] : University of South Florida, 2004. http://purl.fcla.edu/fcla/etd/SFE0000437.

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Kanade, Parag M. "Fuzzy ants as a clustering concept." [Tampa, Fla.] : University of South Florida, 2004. http://purl.fcla.edu/fcla/etd/SFE0000397.

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Fujimoto, Rodrigo Yoshiaki. "Diagnóstico automático de defeitos em rolamentos baseado em lógica fuzzy." Universidade de São Paulo, 2005. http://www.teses.usp.br/teses/disponiveis/3/3151/tde-07012006-111536/.

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Este trabalho apresenta duas metodologias baseadas em lógica fuzzy para automatizar o diagnóstico de defeito em equipamentos mecânicos, além de fazer uma comparação de seu desempenho utilizando um caso experimental. As duas metodologias estudadas são: o sistema de inferência fuzzy e o algoritmo baseado em Fuzzy C-Means. O alarme estatístico é uma metodologia existente atualmente na indústria com este objetivo e que será utilizado neste trabalho para comparação de desempenho. Para realizar os testes, foram desenvolvidos programas que permitiram criar alarmes e sistemas fuzzy utilizando um banco

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13

Samala, Ravi K. "Efficient suspicious region segmentation algorithm for computer aided diagnosis of breast cancer based on tomosynthesis imaging." [Tampa, Fla] : University of South Florida, 2006. http://purl.fcla.edu/usf/dc/et/SFE0001800.

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Quinteiro, José António Teixeira. "Segmentação de individuos no Facebook que gostam de música: abordagem exploratória, recorrendo à comparação entre dois algoritmos, k-means e fuzzy c-means." Master's thesis, Instituto Superior de Economia e Gestão, 2011. http://hdl.handle.net/10400.5/4338.

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Mestrado em Gestão/MBA<br>Para se poder definir os melhores planos estratégicos, as decisões de marketing que se têm que tomar, com o intuito de abordar o mercado, escolher a melhor campanha publicitária, seleccionar o segmento e o tipo de produto ou serviço a oferecer, têm que ter por base o resultado de uma boa análise técnica da informação ou dos dados disponíveis. A escolha do método de segmentação, é de primordial importância, pois os dados que se obtêm podem alterar a estratégia de selecção do mercado alvo e a estratégia de posicionamento dos produtos ou serviços, para além dos custos in

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Stetco, Adrian. "An investigation into fuzzy clustering quality and speed : fuzzy C-means with effective seeding." Thesis, University of Manchester, 2017. https://www.research.manchester.ac.uk/portal/en/theses/an-investigation-into-fuzzy-clustering-quality-and-speed-fuzzy-cmeans-with-effective-seeding(fac3eab2-919a-436c-ae9b-1109b11c1cc2).html.

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Cluster analysis, the automatic procedure by which large data sets can be split into similar groups of objects (clusters), has innumerable applications in a wide range of problem domains. Improvements in clustering quality (as captured by internal validation indexes) and speed (number of iterations until cost function convergence), the main focus of this work, have many desirable consequences. They can result, for example, in faster and more precise detection of illness onset based on symptoms or it could provide investors with a rapid detection and visualization of patterns in financial time

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MACARIO, FILHO Valmir. "Um novo algoritmo de agrupamento semisupervisionado baseado no Fuzzy C-Means." Universidade Federal de Pernambuco, 2009. https://repositorio.ufpe.br/handle/123456789/2383.

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Made available in DSpace on 2014-06-12T15:57:35Z (GMT). No. of bitstreams: 2 arquivo3210_1.pdf: 1552746 bytes, checksum: 98771d23cdfb48745520719f0b3134dd (MD5) license.txt: 1748 bytes, checksum: 8a4605be74aa9ea9d79846c1fba20a33 (MD5) Previous issue date: 2009<br>Conselho Nacional de Desenvolvimento Científico e Tecnológico<br>Nas aplicações tradicionais de aprendizagem de máquina, os classificadores utilizam apenas dados rotulados em seu treinamento. Os dados rotulados, por sua vez, são difíceis, caros, consomem tempo e requerem especialistas humanos para serem obtidos em algumas aplica

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Parker, Jonathon Karl. "Accelerated Fuzzy Clustering." Scholar Commons, 2013. http://scholarcommons.usf.edu/etd/4929.

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Clustering algorithms are a primary tool in data analysis, facilitating the discovery of groups and structure in unlabeled data. They are used in a wide variety of industries and applications. Despite their ubiquity, clustering algorithms have a flaw: they take an unacceptable amount of time to run as the number of data objects increases. The need to compensate for this flaw has led to the development of a large number of techniques intended to accelerate their performance. This need grows greater every day, as collections of unlabeled data grow larger and larger. How does one increase the spe

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Filho, Márcio Coutinho Brandão Côrtes. "Reconhecimento de padrão na biodisponibilidade do ferro utilizando o Algoritmo Fuzzy C-Means." Universidade do Estado do Rio de Janeiro, 2012. http://www.bdtd.uerj.br/tde_busca/arquivo.php?codArquivo=4902.

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Fundação de Amparo à Pesquisa do Estado do Rio de Janeiro<br>Este trabalho apresenta um método para reconhecimento do padrão na biodisponibilidade do ferro, através da interação com substâncias que auxiliam a absorção como vitamina C e vitamina A e nutrientes inibidores como cálcio, fitato, oxalato, tanino e cafeína. Os dados foram obtidos através de inquérito alimentar, almoço e jantar, em crianças de 2 a 5 anos da única Creche Municipal de Paraty-RJ entre 2007 e 2008. A Análise de Componentes Principais (ACP) foi aplicada na seleção dos nutrientes e utilizou-se o Algoritmo Fuzzy C-Means (FCM

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Chahine, Firas Safwan. "A Genetic Algorithm that Exchanges Neighboring Centers for Fuzzy c-Means Clustering." NSUWorks, 2012. http://nsuworks.nova.edu/gscis_etd/116.

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Clustering algorithms are widely used in pattern recognition and data mining applications. Due to their computational efficiency, partitional clustering algorithms are better suited for applications with large datasets than hierarchical clustering algorithms. K-means is among the most popular partitional clustering algorithm, but has a major shortcoming: it is extremely sensitive to the choice of initial centers used to seed the algorithm. Unless k-means is carefully initialized, it converges to an inferior local optimum and results in poor quality partitions. Developing improved method for se

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Hore, Prodip. "Scalable frameworks and algorithms for cluster ensembles and clustering data streams." [Tampa, Fla.] : University of South Florida, 2007. http://purl.fcla.edu/usf/dc/et/SFE0002135.

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Altinel, Fatih. "An Empirical Study On Fuzzy C-means Clustering For Turkish Banking System." Master's thesis, METU, 2012. http://etd.lib.metu.edu.tr/upload/12615027/index.pdf.

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Banking sector is very sensitive to macroeconomic and political instabilities and they are prone to crises. Since banks are integrated with almost all of the economic agents and with other banks, these crises affect entire societies. Therefore, classification or rating of banks with respect to their credibility becomes important. In this study we examine different models for classification of banks. Choosing one of those models, fuzzy c-means clustering, banks are grouped into clusters using 48 different ratios which can be classified under capital, assets quality, liquidity, profitability, in

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FANEGAN, JULIUS BOLUDE. "A FUZZY MODEL FOR ESTIMATING REMAINING LIFETIME OF A DIESEL ENGINE." University of Cincinnati / OhioLINK, 2007. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1188951646.

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Ljubo, Nedović. "Neki tipovi rastojanja i fazi mera sa primenom u obradi slika." Phd thesis, Univerzitet u Novom Sadu, Fakultet tehničkih nauka u Novom Sadu, 2017. https://www.cris.uns.ac.rs/record.jsf?recordId=104792&source=NDLTD&language=en.

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Doktorska disertacija izučava primenu fazi operacija, prvenstveno agregacionih operatora na funkcije rastojanja i metrike. Originalan doprinos teze je u konstrukciji novih funkcija rastojanja i metrika primenom agregacionih operatora na neke polazne funkcije rastojanja i metrike. Za neke tipove agregacionih operatora i polaznih funkcija rastojanja i metrika su ispitane osobine ovako konstruisanih funkcija rastojanja i metrika. Za neke od njih su ispitane performanse pri primeni u segmentaciji slike &bdquo;Fuzzy c-means“ algoritmom.<br>This thesis studies application of fuzzy operations,

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Lopes, Cavalcanti Junior Nicomedes. "Clusterização baseada em algoritmos fuzzy." Universidade Federal de Pernambuco, 2006. https://repositorio.ufpe.br/handle/123456789/2619.

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Made available in DSpace on 2014-06-12T15:59:42Z (GMT). No. of bitstreams: 1 license.txt: 1748 bytes, checksum: 8a4605be74aa9ea9d79846c1fba20a33 (MD5) Previous issue date: 2006<br>Análise de cluster é uma técnica aplicada a diversas áreas como mineração de dados, reconhecimento de padrões, processamento de imagens. Algoritmos de clusterização têm por objetivo particionar um conjunto de dados em clusters de tal forma que indivíduos dentro de um mesmo cluster tenham um alto grau de similaridade, enquanto indivíduos pertencentes a diferentes clusters tenham alto grau de dissimilaridade. Um

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Turner, Kevin Michael. "Estimation of Ocean Water Chlorophyll-A Concentration Using Fuzzy C-Means Clustering and Artificial Neural Networks." Fogler Library, University of Maine, 2007. http://www.library.umaine.edu/theses/pdf/TurnerKM2007.pdf.

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Moghadas, Tabatabaei Zavareh Seyed Mehdi. "A 3D Framework for the Musculoskeletal Segmentation of Magnetic Resonance Images." Thesis, Université d'Ottawa / University of Ottawa, 2015. http://hdl.handle.net/10393/32154.

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In this thesis a new framework is proposed for obtaining the spongy bone, cortical bone, muscle and adipose tissue from MRI data. The method focuses on the accurate extraction of the edges of the target tissues, which is the main drawback of previous works. In this framework six new methods, as listed in section 1.3, are utilized together for improving the result of the segmentation by detecting the relational position of the tissues, acquiring the best possible contribution from the operator in terms of time and efficiency, forward and backward transfer of the segmented tissues at the seed sl

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Chakeri, Alireza. "Scalable Clustering Using the Dempster-Shafer Theory of Evidence." Scholar Commons, 2016. http://scholarcommons.usf.edu/etd/6478.

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Clustering large data sets has become very important as the amount of available unlabeled data increases. Single Pass Fuzzy C-Means (SPFCM) is useful when memory is too limited to load the whole data set. The main idea is to divide dataset into several chunks and to apply fuzzy c-means (FCM) to each chunk. SPFCM uses the weighted cluster centers of the previous chunk in the next data chunks. However, when the number of chunks is increased, the algorithm shows sensitivity to the order in which the data is processed. Hence, we improved SPFCM by recognizing boundary and noisy data in each chunk a

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Rodgers, Sarah. "Application of the fuzzy c-means clustering algorithm to the analysis of chemical structures." Thesis, University of Sheffield, 2004. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.412772.

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Rapstine, Thomas D. "Gravity gradiometry and seismic interpretation integration using spatially guided fuzzy c-means clustering inversion." Thesis, Colorado School of Mines, 2015. http://pqdtopen.proquest.com/#viewpdf?dispub=1602383.

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<p> Gravity gradiometry has been used as a geophysical tool to image salt structure in hydrocarbon exploration. The knowledge of the location, orientation, and spatial extent of salt bodies helps characterize possible petroleum prospects. Imaging around and underneath salt bodies can be challenging given the petrophysical properties and complicated geometry of salt. Methods for imaging beneath salt using seismic data exist but are often iterative and expensive, requiring a refinement of a velocity model at each iteration. Fortunately, the relatively strong density contrast between salt and bac

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Silva, Ana Claudia Guedes. "Identificação de regiões hidrologicamente homogêneas por agrupamento fuzzy c-means no estado do Paraná." Universidade Estadual do Oeste do Paraná, 2018. http://tede.unioeste.br/handle/tede/3760.

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Vargas, Rogerio Rodrigues de. "Uma nova forma de calcular os centros dos Clusters em algoritmos de agrupamento tipo fuzzy c-means." Universidade Federal do Rio Grande do Norte, 2012. http://repositorio.ufrn.br:8080/jspui/handle/123456789/17949.

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Made available in DSpace on 2014-12-17T15:47:00Z (GMT). No. of bitstreams: 1 RogerioRV_TESE.pdf: 769325 bytes, checksum: ddaac964e1c74fba3533b5cdd90927b2 (MD5) Previous issue date: 2012-03-30<br>Coordena??o de Aperfei?oamento de Pessoal de N?vel Superior<br>Clustering data is a very important task in data mining, image processing and pattern recognition problems. One of the most popular clustering algorithms is the Fuzzy C-Means (FCM). This thesis proposes to implement a new way of calculating the cluster centers in the procedure of FCM algorithm which are called ckMeans, and in some variant

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Krovi, Abhishek. "A directed fuzzy c-mean approach for analysis of stained tissues /." Available to subscribers only, 2007. http://proquest.umi.com/pqdweb?did=1324366591&sid=10&Fmt=2&clientId=1509&RQT=309&VName=PQD.

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Beca, Cofre Sebastián. "Clustering Difuso con Selección de Atributos." Tesis, Universidad de Chile, 2007. http://www.repositorio.uchile.cl/handle/2250/104686.

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Nguiffo, Podie Yves. "Segmentation d'images de transmission pour la correction de l'atténué en tomographie d'émission par positrons." Mémoire, Université de Sherbrooke, 2009. http://savoirs.usherbrooke.ca/handle/11143/3993.

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L'atténuation des photons est un phénomène qui affecte directement et de façon profonde la qualité et l'information quantitative obtenue d'une image en Tomographie d'Emission par Positrons (TEP). De sévères artefacts compliquant l'interprétation visuelle ainsi que de profondes erreurs d'exactitudes sont présents lors de l'évaluation quantitative des images TEP, biaisant la vérification de la corrélation entre les concentrations réelles et mesurées.L' atténuation est due aux effets photoélectrique et Compton pour l'image de transmission (30 keV - 140 keV), et majoritairement à l'effet Compton p

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Cupparo, Ilaria. "Region growing and fuzzy C-means algorithm segmentation for PET images of head-neck tumours." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2019. http://amslaurea.unibo.it/18020/.

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The aim of this work, performed at Azienda Ospedialiero Universitaria in Modena, is the implementation and validation of autosegmentation methods of head and neck (H&N) tumor PET images. These autosegmentation processes are important mostly to overcome the problems of manual segmentation, performed by radiotherapist physician, regarding the contouring time (that can reach more than two hours) and the intra-observer and inter-observer variability. Fuzzy C-means (FCM) and Region Growing (RG) algorithms were developed in a MATLAB GUI that allows to choice iteratively the different steps necessary

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Sobjak, Ricardo. "Seleção de variáveis para definição de unidades de manejo através da lógica fuzzy C-means." Universidade Estadual do Oeste do Parana, 2012. http://tede.unioeste.br:8080/tede/handle/tede/405.

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Made available in DSpace on 2017-05-12T14:48:35Z (GMT). No. of bitstreams: 1 Ricardo.pdf: 1705247 bytes, checksum: 435e22f6b6cf7bf51e60d35de482adba (MD5) Previous issue date: 2012-04-09<br>The agricultural production area has important information to understand the response of a crop yield. Precision agriculture helps the farmers on acquiring and treating in correct way the variations found in the area. Management zones (MZ) provide the precision agriculture implementation in a viable and relatively economical way, since they represent homogeneous regions, which can be treated differently. T

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Chang, Jia-Shiuan, and 張嘉軒. "A Study on Generalized Fuzzy C-means Algorithms." Thesis, 2004. http://ndltd.ncl.edu.tw/handle/67662112342801878834.

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碩士<br>中原大學<br>應用數學研究所<br>92<br>In cluster analysis, the fuzzy c-means (FCM) clustering algorithm is the best known and most used method. There are many generalized types of FCM. Some of them such as the conditional fuzzy c-means (CFCM), alternative fuzzy c-means (AFCM), penalized fuzzy c-means (PFCM), partition index maximization (PIM), inter-cluster separation (ICS), maximum entropy-based clustering (MEC) and fuzzy generalized c-means (FGcM) will be studied in this thesis. In fact, these algorithms can be thought of a generalized FCM (GFCM). We proposed a new algorithm based on GFCM. We

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Chang, Chia-Yen, and 張嘉晏. "Fuzzy C-Means Architecture for Fast Image Segmentation." Thesis, 2009. http://ndltd.ncl.edu.tw/handle/15310413308234115126.

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Urbanová, Zuzana. "Optimalizace rozvržení provozu ve firmě Vodárenská akciová společnost a.s." Master's thesis, 2014. http://www.nusl.cz/ntk/nusl-177137.

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Dimploma thesis deals with the optimization of operation distribution in company Vodárenská akciová společnost, a.s. Aim of the thesis is to determine achievement standards of companies branches and to state the capacity reserves for every one of them. Next, using methods of cluster analysis and graph theory, proposing recommendation leading to effective utilization of operation capacity. This is done by optimizing total number of business branches and subsequent creation of new regions. Thesis consists of theoretical and practical part. In this papers theoretical part, hierarchical and nonhie

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Yung-Fu, Tsai. "Multiple-pose Face Detection Using Fuzzy C-means Clustering." 2005. http://www.cetd.com.tw/ec/thesisdetail.aspx?etdun=U0001-2607200512112000.

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Ma, Yen-Ting, and 馬嬿婷. "Sample and cluster weighted fuzzy c-means clustering algorithms." Thesis, 2014. http://ndltd.ncl.edu.tw/handle/tpkev7.

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碩士<br>中原大學<br>應用數學研究所<br>102<br>In fuzzy cluster analysis﹐the fuzzy c-means (FCM) clustering algorithm is the most well-known and used method. Up to now﹐there are various generalizations of FCM. In order to reduce the influences of the clustering results by outliers and noisy points﹐Yu, Yang and Lee (2011) proposed sample-weighted clustering methods that apply the maximum entropy principle to automatically calculate these sample weights so as to increase the robustness of the algorithm. The purpose of this thesis is to give a cluster-weighted version of sample-weighted FCM﹐called the sample an

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Tsai, Yung-Fu, and 蔡永富. "Multiple-pose Face Detection Using Fuzzy C-means Clustering." Thesis, 2005. http://ndltd.ncl.edu.tw/handle/27909134956224960721.

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碩士<br>國立臺灣大學<br>資訊管理學研究所<br>93<br>The challenges for face detection from images come from the variation of poses, facial expressions, occlusions, lighting conditions, and so on. We propose a method for multiple-pose face detection from still images. Our proposed method consists of three phases. First, skin pixels are extracted using a skin color model. Connected component analysis is performed to find the skin regions. Second, before extracting the feature vector of a skin region, we apply edge detection to the region. Our feature vector consists of two parts. The first part is obtained by div

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Huang, Wen-Chun, and 黃文俊. "A Novel Fuzzy Weighted C-Means Method for Classification." Thesis, 2009. http://ndltd.ncl.edu.tw/handle/93482846165969876602.

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碩士<br>國立臺中教育大學<br>教育測驗統計研究所<br>97<br>Many researches show the fuzzy c-means clustering is a powerful tool for partitioning samples into different categories. However, the cost function of the classical fuzzy c-means (FCM) is defined by the distances from data to the cluster centers with their fuzzy memberships. In this study, a new fuzzy clustering algorithm, namely the fuzzy weighted c-means (FWCM), is proposed. In this proposed FWCM, the concept of weighted means used in nonparametric weighted feature extraction (NWFE) is employed for replacing the cluster centers in the FCM. The experiments

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Li, Jui-Pin, and 李瑞彬. "Color Quantization with Fuzzy C-Means for Cells Counting." Thesis, 2016. http://ndltd.ncl.edu.tw/handle/48788214320203672999.

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碩士<br>國立暨南國際大學<br>電機工程學系<br>104<br>The demand for biological testing becomes more and more important as humans pay more attention to their health. Conventional manual cell counting process in biological testing is laborious and rather time consuming. Besides, the accuracy of counting results relies on the ability of professional operators. The physical and psychological conditions of the operators often affect the accuracy of the observation results. Accordingly, we propose in this thesis an automated counting tool with a fast and accurate way to solve the uncertainty under the microscope obse

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Liu, Shih-Hua, and 劉士華. "Sperm Whale's Voiceprint Classification via Fuzzy C-means Clustering." Thesis, 2016. http://ndltd.ncl.edu.tw/handle/52459398101146752146.

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碩士<br>國立臺灣海洋大學<br>電機工程學系<br>104<br>Sperm whale is one species of odontoceti mammals, possesses highly developed intelligence, performs mutual communication and social activities by sending audio signals of different pulse sequence combinations. To understand the ecology of the sperm whale, we can perform voiceprint’s feature analysis and clustering from the recorded audio signals. However, background noise is corrupted into the original whale’s sound during the sound recording process, thus reducing the quality of the original whale’s sound and leading to the hardship of voiceprint’s feature a

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Ssu-Min, Yang, and 楊斯閔. "Kernel-Based Fuzzy c-Means Clustering Algorithm Hadrware Implementation." Thesis, 2011. http://ndltd.ncl.edu.tw/handle/72322960297601595511.

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Chu, Chih-Yu, and 朱致宇. "Robust Fuzzy C-Means Clustering Algorithm for Interval Data." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/b8ne4x.

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碩士<br>中原大學<br>應用數學研究所<br>106<br>Abstract In fuzzy clustering, the fuzzy c-means (FCM) algorithm is the most widely used clustering method. Many extensions of FCM had been proposed in the literature. However, the FCM algorithm and its extensions are usually affected by initializations and parameter selection with a number of clusters to be given a priori. Although there were some works to solve these problems in FCM, there is no work for FCM to be simultaneously robust to initializations and parameter selection under free of the fuzziness index m without a given the number of clusters and param

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Chung-LiTai and 戴崇禮. "Road Detection Using Dynamic Fuzzy Logic and Fuzzy c-Means Line-Clustering." Thesis, 2010. http://ndltd.ncl.edu.tw/handle/92965856362542310868.

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博士<br>國立成功大學<br>航空太空工程學系碩博士班<br>98<br>In this study, the RGB color ratio space is proposed and defined according to a reference color Different to traditional distance measurement, a road color model is determined by an ellipse area in the RGB ratio space enclosed by the estimated boundaries. A proposed dynamic fuzzy logic, which fuzzy membership functions are defined according to estimated boundaries, is introduced to implement clustering rules. Therefore, each pixel will have its own fuzzy membership function corresponding to its intensity. Fuzzy c-means line-clustering which integrate linea

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Lin, Chih-Ho, and 林志和. "Fuzzy C-Means with GA for Searching the Optimal Route." Thesis, 2008. http://ndltd.ncl.edu.tw/handle/37883608494552701816.

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碩士<br>嶺東科技大學<br>資訊科技應用研究所<br>96<br>Because of the development of Internet, the shopping in the network’s platform and quickly acquiring the purchased goods are an essential consumption behavior. Furthermore, based on large-scale economy, many multiple stores, which goods are uniformly management and distribution, are rapidly established. To possess quick and effective dispatching goods is not only satisfied with the demand for customers and improving the quality of the service, but also decreasing the capitalized cost. This work will address the optimal routes for all the multiple stores, i.e.

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Simbolon, Agus Pahala, and 沈孝明. "Metaheuristic-based fuzzy c-means Algorithm for Apparel Sizing System." Thesis, 2014. http://ndltd.ncl.edu.tw/handle/97392734579427637586.

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碩士<br>國立臺灣科技大學<br>工業管理系<br>102<br>Sizing system is essential for apparel design and production. Accurate size of apparel is related to customer satisfaction and manufacturing. Several researches had been proposed to create sizing system. This study aims to develop a new sizing system for anthropometry data using novel data mining approach. This study employs metaheuristic-based clustering techniques to determine a new standard sizing system for apparel industry. Through measuring anthropometry of 912 objects (598 males and 304 females) aged between 18 and 25, this study proposes a sizing syste

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