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 45 for “"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, comparado com …
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Improvisation of fuzzy c-means method and fuzzy linear regression model in predicting manufacturing income
… predicting manufacturing income by using fuzzy c-means (FCM) method, apply existing methods such as multiple linear regression (MLR) and fuzzy linear regression (FLR) as proposed by Tanaka and Ni in predicting manufacturing income, improvise of FCM method and FLR model proposed by Zolfaghari in …
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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
… 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 are …
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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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An enhanced progressive fuzzy clustering approach to pattern recognition
… has been developed. A Possibilistic Fuzzy c-Means algorithm(PFCM), which is also new, has been formulated to investigate properties of fuzzy clustering. PFCM extends the useability of the Fuzzy c-Means (FCM) algorithm by generalisation of the membership function.
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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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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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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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Le groupage flou avec AFSA: methodologie et application à l'analyse des sites Web
… de l'algorithme C-Moyen flou (Fuzzy C-Means : FCM) et améliorer ses résultats."
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I2MAPREDUCE: DATA MINING FOR BIG DATA
… 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 mining …
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Dental Variation in Central Mexican Latinx Individuals
… 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 information …
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A 3D Framework for the Musculoskeletal Segmentation of Magnetic Resonance Images
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 …
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Multispectral segmentation of whole-brain MRI
… 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 segmented …
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Aportación a la extracción de conocimiento aplicada a datos mediante agrupamientos y sistemas difusos
… 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 datos. Esto …
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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
… 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 insights …
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Laboratory studies of the biodegradation of chemically dispersed oil: effect of droplet size and nutrient amendment
… (125 rpm) speed mixing, respectively. HN and LN means high and low nutrient, respectively. C means control, which was HSLN with azide added to inhibit bacterial activity, and subsequently oil biodegradation. The initial median oil droplet was 3.00 microns, 3.15 microns, 4.34 microns, and 3.48 …
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Online clustering with single-pass topology based fuzzy clustering algorithm
… Generic offline clustering methods such as K-Means, C-Means and others are computationally expensive. The computational burden of these methods increases non-linearly with the size of the data set. In addition these methods usually require a good amount of supervised knowledge yielding a …
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Improving Attenuation Correction In Hybrid Positron Emission to mography
… 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 to …
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