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 229 for “"clustering algorithm"”.
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COGNITIVE RADIO CLUSTERING ALGORITHM FOR SWARMS USING NEURAL NETWORKS
… cognitive radio concepts and machine learning algorithms to develop a dynamic clustering technique within the network that will optimize resource allocation. Three approaches are proposed to train a neural network to find an optimal spectrum allocation. Even though the proposed algorithm did …
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Development of a clustering algorithm for universal color image segmentation
Submitted in fulfillment of the requirements of the degree of Doctorate in Information Technology, Durban University of Technology, Durban, South Africa, 2022.
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Online clustering with single-pass topology based fuzzy clustering algorithm
Online clustering is of significant interest for real-time data analysis. 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 …
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An Arithmetic-Based Deterministic Centroid Initialization Method for the k-Means Clustering Algorithm
<p>One of the greatest challenges in k-means clustering is positioning the initial cluster centers, or centroids, as close to optimal as possible, and doing so in an amount of time deemed reasonable. Traditional fc-means utilizes a randomization process for initializing these centroids, and poor …
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Modeling species geographic distributions in aquatic ecosystems using a density-based clustering algorithm
… likely to be present, based on a density-based clustering algorithm as a proxy of the realized niche (i.e., abiotic, and biotic environmental conditions occupied by the organism). Supported by ecological theories and methods, my central hypothesis is that because density-based clustering …
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A Fast Clustering Algorithm Merging The Expectation Maximization Algorithm and Markov Chain Monte Carlo
Clustering is an important problem in Statistics and Machine Learning that is usually solved using Likelihood Maximization methods, of which the Expectation-Maximization algorithm (EM) is the most common. In this work we present an algorithm merging Markov Chain Monte Carlo methods with the EM …
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RCP: A temporal clustering algorithm for real-time controller placement in software-defined networks
Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2024-03-01 without embargo terms
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NCIS: a network-assisted co-clustering algorithm to discover cancer subtypes based on gene expression
… In this thesis, we introduce a new co-clustering algorithm for cancer subtype identification, which combines the information of gene networks to simultaneously group samples and genes into biologically meaningful clusters. We call our method network-assisted co-clustering for the …
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A comparison of driving characteristics and environmental characteristics using factor analysis and k-means clustering algorithm
… identified different driver types using k-means clustering, and studied how the same drivers map in each classification domain. The research consists of two study cases. In the first study case, a new variable is proposed and then is used for classification. The drivers were divided into three …
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All jobs are equal, but some jobs are more equal than others: what a clustering algorithm reveals about the labour market segmentation in South Africa
… from equalising. In this paper, I make use of a clustering algorithm to identify these informal and formal segments in the labour market. I apply this methodology to a nationally representative panel dataset of employed South Africans. I find that employed South Africans fall into one of three …
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On Clustering Images of Objects
… simultaneously, we do not have a reasonably good clustering algorithm yet. A few candidate algorithms are tested and the result are given in this thesis. Finding an effective similarity measure for both changes still remains as an open problem.
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Learning Statistical and Geometric Models from Microarray Gene Expression Data
… structure, we propose a novel statistical data clustering and visualization algorithm that is comprehensively effective for multiple clustering tasks and that overcomes some major limitations of existing clustering methods. The proposed clustering and visualization algorithm performs …
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Accelerating lattice scoring of automatic speech recognition through acoustic pre-pruning on GPU
This thesis introduces an acoustic pre-pruning algorithm that speeds up lattice scoring for GMM based ASR systems, and a constrained agglomerative clustering algorithm that makes it possible to maintain the advantage of the new algorithm in a GPU implementation. The implementation undergoes 2% to …
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Large-scale consensus clustering and data ownership considerations for medical applications
… of data storage, the development of Big Data algorithms in other domains and the Health Information Technology for Economic and Clinical Health (HITECH) Act's $20 billion incentive for hospitals to install and use Electronic Health Record (EHR) systems. The data being collected presents an …
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A novel intersection-based clustering scheme for VANET
… into subgroups called clusters. Many such clustering algorithms have been proposed, but none have yet been determined to be optimal. This dissertation puts forth a new passive clustering approach that has the key advantage of a significantly reduced overhead. The reduced overhead of passive …
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Adaptive clustering and transmission range adjustment for topology control in wireless sensor networks
… considerable body of research. Topology control algorithms can be divided into duty-cycle-based algorithms and transmission-power-based algorithms according to their energy saving approaches. By dynamically integrating the two approaches, I have developed a two-level topology control strategy to …
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Gaining Computational Insight into Psychological Data: Applications of Machine Learning with Eating Disorders and Autism Spectrum Disorder
… and Autism Spectrum Disorder (ASD). We explore clustering algorithms as well as virtual reality (VR).</p> <p>Our first study employs the k-means clustering algorithm to explore eating disorder behaviors. Our results show that the Eating Disorder Examination Questionnaire (EDE-Q) and Clinical …
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Classifying teams in the NBA with player behavioral data
… to shoot or make another pass. I apply a k-means clustering algorithm to cluster teams based on their starting lineup behavior data; the clusters show different team makeups within the behavioral data collected. In particular, the clustering identified pass-heavy vs dribble-heavy offenses, and …
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Interpretation and clustering of handwritten student responses
This thesis presents an interpretation and clustering framework for handwritten student responses on tablet computers. The ink analysis system is able to capture and interpret digital ink strokes for many types of classroom exercises, including graphs, number lines, and fraction shading problems. …
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DIGITAL IMAGE PROCESSING OF LANDSAT IMAGERY
… developed as as aid to the analysis of pixel clustering and distribution for Band Ratio and Band Differential methods of image enhancement. The vector connectedness 4-dimensional clustering algorithm is choosen as a basis for this study involving digital image classification. The thesis also …
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