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 98 for “"Clustering Techniques"”.
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Optimal clustering techniques for metagenomic sequencing data
Metagenomic sequencing techniques have made it possible to determine the composition of bacterial microbiota of the human body. Clustering algorithms have been used to search for core microbiota types in the vagina, but results have been inconsistent, possibly due to methodological differences. We …
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Hierarchical Clustering Techniques for Energy-efficient Algorithms in WSNs
… by proposing a novel set of Energy-Adaptive Clustering Protocols (ECP) for energy-efficiency in WSNs. The proposed routing strategy takes on several approaches to improve energy efficiency in WSNs. This functional set of protocols are integrated with each other. The proposed novel solutions …
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Application of Clustering Techniques to the Classification of Marine Phytoplankton
… is given with sections on Flow Cytometry, Clustering and so on. This includes a literature survey on research into fuzzy clustering algorithms, with a section specifically related to Flow Cytometry. Details are given about the data sets and the software used, and the clustering algorithms …
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MALDI-TOF MS Data Processing Using Wavelets, Splines and Clustering Techniques.
… for adaptive denoising, multivariable statistics techniques such as clustering analysis, and signal processing techniques to evaluate the complicated biological signals. A MatLab implementation shows the processing steps consecutively including step-interval unification, adaptive wavelet …
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Evaluation of clustering techniques for generating household energy consumption patterns in a developing country
This work compares and evaluates clustering techniques for generating representative daily load profiles that are characteristic of residential energy consumers in South Africa. The input data captures two decades of metered household consumption, covering 14 945 household years and 3 295 848 daily …
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Empirical analysis of rough set categorical clustering techniques based on rough purity and value set
Clustering a set of objects into homogeneous groups is a fundamental operation in data mining. Recently, attention has been put on categorical data clustering, where data objects are made up of non-numerical attributes. The implementation of several existing categorical clustering techniques is …
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Automated Parsing of Flexible Molecular Systems using Principal Component Analysis and K-Means Clustering Techniques
… (ii) use principal component analysis (PCA) and clustering to find and investigate conformational families within the ensemble, (iii) separate and visualize conformational families in a user-friendly manner, and (iv) convey to the user how conformational families were delineated by way of …
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Percussion Based Detection Method for Localization of Pipe Inspection Gauge using Advanced Machine Learning Classification and Clustering Techniques.
… and time consuming. Various PIG localization techniques have been invented, using a whole suite of advanced instruments, from global positioning to magnetic based tracking to percussion based. Percussion based detection in particular stands out due to its ability to be done in all pipeline …
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Automated Discovery of Big Data Workload Types
… categories that these workloads can belong. Clustering techniques are applied in this research to detect Apache Spark and Hadoop workloads independent of historical data. Clustering techniques are compared in terms of different evaluation metrics, and the ones with the highest performance are …
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An investigation into the application of artificial neural networks and cluster analysis in long-term load forecasting
… according to their pattern of use using clustering techniques in order to produce an effective long-tenn load forecast.
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Investigating Real-Time Sonar Performance Predictions Using Beowulf Clustering
… conditions. This paper discusses how Beowulf clustering techniques were investigated and applied to achieve real-time sonar performance prediction capabilities based on commercially off the shelf (COTS) hardware and software. A sonar system measures ambient noise in real-time. Based on the …
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Characterization of unstructured video
… Models for video analysis. Secondly, we examine clustering as an approach for characterization of unstructured video. Clustering alleviates some of the common problems with "query-by- example" and presents groupings that rely on the user's abilities to make relevant connections. The clustering …
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Some methods and models for analyzing time-series gene expression data
… such analysis. The methods developed include new clustering techniques based on nonparametric Bayesian procedures, and a confirmatory methodology to validate that the clusters produced by any of these methods have statistically different mean paths.
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Biological Networks: Modeling and Structural Analysis
… functional modules through the use of graph clustering techniques. The application of earlier graph clustering techniques to proteomic networks does not yield good results due to the high error rates present, and the small-world and power-law properties of these networks. We discuss the …
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Analysing fuel transactions of government vehicles in the Eastern Cape, South Africa
… the application of exploratory data analysis, clustering techniques, and predictive modelling, the research uncovers valuable insights that can be used to optimise fuel consumption and detect fraudulent activities within the fleet. Univariate and bivariate analyses reveal distinct patterns in …
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A fast seeding technique for k-means algorithm.
The k-means algorithm is one of the most popular clustering techniques because of its speed and simplicity. This algorithm is very simple and easy to understand and implement. The first step of this algorithm is choosing k initial cluster centers. The way that this set of initial cluster centers …
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Normal Mixture Models for Gene Cluster Identification in Two Dimensional Microarray Data
… discussed. Comparing the results of different clustering methods is complicated by the arbitrariness of the cluster labels. Methods for re-labeling clusters to assess the agreement between the results of different clustering techniques are proposed. Microarray data involve large numbers of …
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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 which clustered by the two different clustering categories. Then, to assess the models developed by using three measurement methods which are deviance (D), Akaike Information Criteria (AIC) and Bayesian Information Criteria (BIC). Lastly, the validation of model developed by comparing …
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Using Graph Clustering to Analyze the Spread of an Infectious Disease on a Random Large Social Network Graph
… network graph. The goal is to determine if graph clustering techniques are a viable option to reduce workload of analyzing of a large data set. A random graph generator was developed using characteristics from the Forest Fire Model. We then use this graph to model the spread of an infectious …
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A hierarchical approach to improve the ant colony optimization algorithm
… ACO runtime increases dramatically. As a result, clustering nodes into groups is an effective way to reduce the size of the problem while leveraging the advantages of the ACO algorithm. The method for recombining groups of nodes is explored by treating the graph as a hierarchy of clusters, and …
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