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 10 of 10 for “"fuzzy-c-means (FCM)"”.
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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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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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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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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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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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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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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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Multimedia computer networks quality of service techniques evaluation and development.
… applications over computer networks means that Quality of Service (QoS) needs to be managed in an efficient manner. Network QoS management in this thesis refers to evaluation and improvement of QoS provided by integrated wired and wireless computer networks. Evaluation of QoS aims to …
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Robust techniques and applications in fuzzy clustering
… based clustering techniques, such as Fuzzy c-Means (FCM) and its variants is addressed. In this work, two novel and robust clustering schemes are presented and analyzed in detail. They approach the problem of robustness from different perspectives. The first scheme scales down the FCM …
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Desarrollo eficiente de algoritmos de clasificación difusa en entornos Big Data
… caso, se utilizó una modificación del algoritmo Fuzzy C-Means (FCM), mFCM, como técnica de discretización con el objetivo de convertir los datos de entrada de continuos a discretos. Este proceso tiene especial importancia debido a que hay determinados algoritmos que necesitan valores discretos …