Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 20 of 40 for “"Fuzzy C-Means"”.
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Um novo algoritmo de agrupamento semisupervisionado baseado no Fuzzy C-Means
… semi-supervisionado baseado no algoritmo Fuzzy C-Means. Também, apresenta uma validação cruzada para o contexto de algoritmos semi-supervisionados. Estudos experimentais são apresentados. Primeiro, o algoritmo semi-supervisionado proposto é avaliado com dados completamente rotulados, …
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Improvisation of fuzzy c-means method and fuzzy linear regression model in predicting manufacturing income
… within the data have to be dealt with. Thus, fuzzy structure system is considered. The objectives of this study were to: determine suitable cluster for predicting manufacturing income by using fuzzy c-means (FCM) method, apply existing methods such as multiple linear regression (MLR) and fuzzy …
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A new hybrid model of dengue incidence rate using negative binomial generalised additive model and fuzzy C means model a case study in Selangor
… of new datasets after clustered by district and Fuzzy C-Means Model (FCM). Thirdly, the development of models using the existing dataset and the new datasets which clustered by the two different clustering categories. Then, to assess the models developed by using three measurement methods which …
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An enhanced progressive fuzzy clustering approach to pattern recognition
… progressive clustering approach, involving fuzzy clustering algorithms and fuzzy neural networks, to solve some practical problems of pattern recognition. A new fuzzy clustering framework, referred to as Cluster Prototype Centring by Membership (CPCM), has been developed. A Possibilistic …
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Exploratory Study of Fuzzy Clustering and Set-Distance Based Validation Indexes
… it addresses the con-vergence speed of fuzzy c-means family of algorithms and cluster validation. The fuzzy c-meansclustering algorithm and its objective function is studied along with a literature review of thespeed of clustering algorithms. After careful examination, several objective …
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Intercomparison of medical image segmentation algorithms
… region growing, and clustering methods such as k-means and fuzzy c-means algorithms. The main objective of this project was to investigate a representative number of different algorithms and compare their performance. Image segmentation algorithms, including thresholding, region growing, …
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A study of type-2 fuzzy clustering
Fuzzy C-means (FCM) has been a prominent clustering algorithm for a long time. It was extended to a type-2 framework by the linguistic fuzzy C-means (LFCM) algorithm that operates on vectors of fuzzy numbers utilizing the extension principle, the decomposition theorem, and interval analyses. The …
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Le groupage flou avec AFSA: methodologie et application à l'analyse des sites Web
… la performance de l'algorithme C-Moyen flou (Fuzzy C-Means : FCM) et améliorer ses résultats."
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I2MAPREDUCE: DATA MINING FOR BIG DATA
… includes iteration algorithms such as PageRank, Fuzzy-C-Means(FCM), Generalized Iterated Matrix-Vector Multiplication(GIM-V), Single Source Shortest Path(SSSP). The main purpose of this project is to reduce input/output overhead, to avoid incurring the cost of re-computation and avoid stale data …
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Dental Variation in Central Mexican Latinx Individuals
… Biological distance, or biodistance statistics, fuzzy c-means analyses, and linear discriminant function analysis were utilized to analyze this variation. The Latinx population has been shaped through 500 years of microevolutionary forces due to European colonization of the Americas. Genetic …
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Performance analysis of automatic techniques for tissue classification in magnetic resonance images of the human brain
… Minimum Distance) and two unsupervised (Hard C Means, Fuzzy C Means) classification algorithms is compared under varying conditions of MR imaging artifacts. The Artificial Neural networks classifier was observed to be the best overall performer
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Multispectral segmentation of whole-brain MRI
… colored image. This technique is based on the fuzzy c-means (FCM) clustering algorithm. The MR data sets are used to form five-dimensional feature vectors. These vectors are segmented by FCM into six tissue classes for normal brains and nine tissue classes for human brains with tumors. The …
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Aportación a la extracción de conocimiento aplicada a datos mediante agrupamientos y sistemas difusos
… de agrupamiento híbrido PFCM (Possibilistic Fuzzy c- Means) al que hemos incorporado una mejora, cuyo algoritmo hemos denominado GKPFCM (Gustafson-Kessel Possibilistic Fuzzy c-Means), y que permite encontrar grupos con formas más aproximadas a las distribuciones naturales de los grupos de …
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Development of an advanced deep learning and neural network method for automatic early detection of mastitis in dairy cattle : A thesis submitted in partial fulfilment of the requirements for the Degree of Doctor of Philosophy at Lincoln University
… employed to capture the mastitis spectrum. Then, Fuzzy C-Means (FCM) clustering was applied to the SOM map, identifying five health states—healthy, early subclinical, subclinical, late subclinical and clinical—despite the absence of specific labels for the subclinical stages. Building on the …
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Improving Attenuation Correction In Hybrid Positron Emission to mography
… registration algorithm based on a modified fuzzy c-means clustering method and gradient correlation was developed and validated to perform automatic registration in cardiac PET/CT data of different breathing protocols. A free- breathing MR protocol and post-process algorithm were developed …
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Epileptic Seizure Detection And Prediction From Electroencephalogram Using Neuro-Fuzzy Algorithms
… presents innovative approaches based on fuzzy logic in epileptic seizure detection and prediction from Electroencephalogram (EEG). The fuzzy rule-based algorithms were developed with the aim to improve quality of life of epilepsy patients by utilizing intelligent methods. An adaptive …
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Building Energy Profile Clustering Based on Energy Consumption Patterns
… unsupervised clustering methods such as k-means, hierarchical clustering, fuzzy c-means, and self-organizing map were presented on building load shape portfolios, and their performance were quantitatively and qualitatively compared. The evaluation was carried out on real energy data of ~250 …
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Urban computing using call detail records : mobility pattern mining, next-location prediction and location recommendation
… 3) and infer home and workplaces using K-means Clustering and Fuzzy C-means Clustering. The proposed method was implemented on MIT Reality Mining data, by which we demonstrate that with inference rates of 56% and 82%, the method can improve 79% and 34% in accuracy respectively in home and …
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Neighbourhood profiling and classification for community safety
… The final partition was created using the fuzzy c-means clustering technique, but alternative techniques were also employed and levels of agreement between the different results were measured. The design process also involved measuring the ability of different partitions to discriminate …
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