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 36 for “"k-means algorithm"”.
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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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Optimal Clustering: Genetic Constrained K-Means and Linear Programming Algorithms
… this dissertation, we propose two constrained k-means algorithms: Linear Programming Algorithm (LPA) and Genetic Constrained K-means Algorithm (GCKA). Linear Programming Algorithm modifies the k-means algorithm into a linear programming problem with constraints requiring that each cluster have m …
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An unsupervised approach to COVID-19 fake tweet detection
… the potential of unsupervised machine learning algorithms in differentiating between genuine and fake COVID-19 news shared on Twitter. The methodology includes a literature review, experimental analysis, and the utilization of a Twitter dataset. Methods: The study used both Mini-Batch K-means …
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Web-based data visualization and optimization methods for applications in urban planning
… new entities within the city using a modified k-means algorithm.
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Inductive Monitoring Systems: A CubeSat Ground-Based Prototype
… use. This program consisted of two main algorithms, one for learning and one for monitoring. The learning algorithm creates the nominal knowledge bases and was developed using three data mining algorithms: the gap statistic method to find the optimal number of clusters, the K-means++ …
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Principal points, principal curves and principal surfaces
… are the theoretical counterparts of cluster means obtained by the k-means algorithm. Principal curves defined by Hastie (1984), are smooth one-dimensional curves that pass through the middle of a p-dimensional data set, providing a nonlinear summary of the data. In this dissertation, details …
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Faster k-means clustering.
The popular k-means algorithm is used to discover clusters in vector data automatically. We present three accelerated algorithms that compute exactly the same clusters much faster than the standard method. First, we redesign Hamerly's algorithm to use k heaps to avoid checking distance bounds for …
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Local Optima in K-Means Clustering
… study of the properties of local optimality in K-means clustering is pursued. In doing so, it is shown that several of the commercial software packages prove to be inadequate in their treatment of the K-means algorithm, resulting in the proposal of an alternative method based on several thousand …
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Dynamic Chemical Shift Imaging For Image-Guided Thermal Therapy
… imaging (MRTI) is recognized as a noninvasive means to provide temperature imaging for guidance in thermal therapies. The most common method of estimating temperature changes in the body using MR is by measuring the water proton resonant frequency (PRF) shift. Calculation of the complex phase …
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A Radial Basis Function Approach to a Color Image Classification Problem in a Real Time Industrial Application
… radial basis functions are calculated by the k-means clustering algorithm. We examine the k-means algorithm in terms of starting criteria, the movement rule, and the updating rule. The dilations of the radial basis functions are calculated using a statistical method. Learning classifier systems …
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Harnessing the power of intersection for data disaggregation: a novel similarity measure and unsupervised data-driven classification method applied to financial contagion
… harder to distinguish between themselves. An algorithm is constructed in order to run the proposed white-box method, which dynamics may be readily interpreted through a clear data visualisation. The conceptual benefits and caveats of the proposed method is compared to the well-established and …
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Predicting cardiovascular risks using pattern recognition and data mining.
… and self organizing maps, KMIX and WKMIX algorithms for unsupervised clustering. The Physiological and Operative Severity Score for enUmeration of Mortality and morbidity (POSSUM), and Portsmouth POSSUM (PPOSSUM) are introduced as the risk scoring systems used in British surgery, which …
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Colour analysis and the classification of fruit
… and the unsupervised methods of the K-means algorithm and the ISODATA classification approach. The ICS Texicon computer spectrophotometer (ICS Texicon Spectraflash Manual (1991)) was used to check the performance of most of the colour systems described by analyzing apple sample colours
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Wide Area Power System Monitoring Device Design and Data Analysis
… for further analysis. A new event detection algorithm, the k-means algorithm, is also presented in this paper. The algorithm is proposed as a simple and fast alternative to the current detection method. Next, this thesis examines several GPS modules and recommends one for a replacement of the …
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An autonomous host-based intrusion detection and prevention system for Android mobile devices. Design and implementation of an autonomous host-based Intrusion Detection and Prevention System (IDPS), incorporating Machine Learning and statistical algorithms, for Android mobile devices
… by exploiting statistical and machine learning algorithms. That is, it builds a data-driven model for benign behaviour and looks for the outliers considered as suspicious activities. Any observation failing to match this model triggers an alert and the preventive agent takes proper …
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Evaluation of clustering techniques for generating household energy consumption patterns in a developing country
… social and economic dimensions. Different algorithms, normalisation and pre-binning techniques are evaluated to determine the best clustering structure. The study shows that normalisation is essential for producing good clusters. Specifically, unit norm produces more usable and more …
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A new class of functions for describing logical structures in text
… from this corpus were clustered with a k-means algorithm using cadence data. Precision and recall performances were computed for the results, and a chi-squared cross-tabulation test was used to determine the statistical significance of the clustering results. Precision and recall were …
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Characterization and prediction of air traffic delays
… The proposed model uses Random Forest (RF) algorithms, considering both temporal and network delay states as explanatory variables. In addition to local delay variables that describe the arrival or departure delay states of the most influential airports and origin-destination (OD) pairs in …
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Machine Learning-Driven Corrosion Detection and Classification in Pipelines
… processing techniques and machine learning algorithms. The study is grounded in a positivist research philosophy, employing a deductive approach to apply established theories in a real-world context. Data for this research was collected from a project conducted by a petrochemical company in …
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Dynamic Aggregation of Grid-tied Inverters
… PV array, a maximum power point tracking (MPPT) algorithm, and a dc-link capacitor are incorporated into the three-phase inverter model. Furthermore, a network-cognizant aggregation approach for distribution networks comprising grid-tied inverters is developed. Inverters are clustered based on …
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