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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 30 for “"DBSCAN"”.
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Modeling Vessel Behaviours By Clustering Ais Data Using Optimized DBSCAN
… an enhanced density-based spatial clustering (DBSCAN) method to model vessel behaviors. The proposed methodology enhances the DBSCAN clustering performance by integrating the Mahalanobis Distance metric that considers the correlations of the points representing the locations of the vessels. The …
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Load Frequency Control for Electric Water Heater Using DBSCAN Algorithm in real-time
… Spatial Clustering of Applications with Noise (DBSCAN) algorithm to dynamically group water heaters based on their thermal state and operational flexibility, enabling coordinated response to grid frequency deviations.<br/><br/>The methodology integrates real-time grid frequency monitoring with …
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Automated Discovery of Big Data Workload Types
… with the highest performance are introduced. The DBSCAN algorithm has shown the best performance and adequacy with 71% and 80% for the Purity, and Windows Type Accuracy (Awt), respectively. Ultimately, the Incremental DBSCAN algorithm and Den-Stream (an online version of DBSCAN) are presented as …
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A Study on Leveraging Generative Artificial Intelligence and Text Clustering to Support Vendors
… the effectiveness of clustering algorithms like DBSCAN and K-Means for grouping text feedback based on semantic similarity and on the employment of retrieval augmented generation (RAG) for extracting actionable insights. Our findings indicate a relative effectiveness of K-Means over DBSCAN in …
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De-interleaving of Radar Pulses for EW Receivers with an ELINT Application
… spatial clustering of applications with noise (DBSCAN) is a clustering algorithm that has not been used before in de-interleaving; in this dissertation, it has proved to be effective. DBSCAN was thus selected as a component of the proposed de-interleaving solution due to its advantages over …
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Visualisation of Mental Health Community Service Patient Pathways
… Hierarchical Agglomerative Clustering (HAC), DBSCAN, and K-Medoids, were applied to both vectorial and syntactical representations of patient pathways. The results indicate that HAC, with a vectorial representation, was the most effective approach, followed by DBSCAN, while K-Medoids and …
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Coping With New Challengens for Density-Based Clustering
… clustering notion underlying the algorithm DBSCAN and its hierarchical extension OPTICS has been proposed recently, being one of the most successful approaches to clustering. In this thesis, our aim is to advance the state-of-the-art clustering, especially density-based clustering by …
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Density-Based Clustering of High-Dimensional DNA Fingerprints for Library-Dependent Microbial Source Tracking
… a clustering algorithm based on the original DBSCAN algorithm which removes points from the search space after they have been seen once. We also present a new method for comparing Pyroprints which is algebraically related to the current method. The method has mathematical properties which make …
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Fast Parallel Algorithms and Library for Spatial Clustering and Computational Geometry
… parallel algorithms for exact and approximate DBSCAN, which match the work complexity of the best sequential algorithms while maintaining low depth. Extensive experiments demonstrate that our algorithms achieve massive speedup over existing algorithms and can efficiently process large-scale …
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Automatic Extraction of Joint Characteristics from Rock Mass Surface Point Cloud Using Deep Learning
… using the Density-Based Scan with Noise (DBSCAN) clustering algorithm. Subsequently, the orientations of the identified joint surfaces are computed by fitting least-square planes using the Random Sample Consensus (RANSAC). Finally, the joint planes are classified into different joint sets, …
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Estimation of ion drift velocity vector in F region ionosphere based on incoherent scattered pulse data using machine learning technique
… improve accuracy, a machine learning algorithm, DBSCAN (density-based spatial clustering of applications with noise), is used to distinguish the data segments from the noise segments. The LOS velocities of backscattered pulses are projections of the drift velocity vectors on LOS direction unit …
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Path planning of agricultural UAVs for combined coverage and spot spraying application
… Spatial Clustering of Applications with Noise (DBSCAN) with principal component analysis (PCA) and adapting the traveling salesman problem (TSP) algorithms, the system generates optimized UAV trajectories that reduce operational time, chemical usage, and ecological impact. The algorithm …
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Decoding team performance in a self-organizing collaboration network using community structure
… network and applied both spectral methods and DBSCAN to identify communities of teams while remaining ignorant of their performances. Finally, I generated cross-cluster performance distributions, evaluated the significance of communities found, and calculated a predictor statistic. Using …
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Tracking Dust Plumes and Identifying Source Areas Using Spatiotemporal Clustering of Remote Sensing Data
… airborne dust in images. We then use the DBSCAN machine learning algo-rithm to cluster pixels into plumes based on their spatial and temporal connectivity. Through careful analysis and processing, we are able to analyze properties such as the storm’s source area, distance traveled, and …
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DATA MINING TECHNIQUES ON VOLCANO MONITORING
… techniques is reported. In particular, DBSCAN Algorithm and SVM (Support Vector Machines) are discussed. Finally, Chapter 6 shows some geophysical applications where data mining techniques are applied for volcano data analysis and surveillance purpose. In particular, an application for …
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Evaluating and predicting urban performance through behavioral patterns in temporal telecom data : a case study in Andorra
… Spatial Clustering of Application with Noise (DBSCAN) to evaluate performance of urban space. By leveraging regression models for machine learning, the research attempts to match characteristics of human behavioral patterns of clustering including persistence, size and diversity, with discrete …
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Fast Clustering Using a Grid-Based Underlying Density Function Approximation
… improvements over standard algorithms such as DBSCAN with no accuracy penalty. Furthermore, the algorithm is also suitable for use with parallel and distributed systems, as an implementation with Apache Spark showed proper parallel scaling with low data set sizes required to overtake the serial …
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Integrating Principal Component Analysis and Deep Learning Methods for Data Representation and Image Denoising
… class-relevant structure, as confirmed by DBSCAN and K-Means clustering in the reduced space. To illustrate the properties of PCA, we consider a classical problem of image denoising where four denoisers, including linear PCA, kernel PCA, a shallow autoencoder, and a deep learning model …
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Machine Learning Methods for Churn Prediction and Infrastructure Resilience
… Leveraging spatial-temporal clustering methods (DBSCAN) and advanced neural network architectures, I develop a model to attribute historical outages to extreme weather events. Further, I integrate this model with future climate scenarios from CMIP5 projections using Monte Carlo simulations, …
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Approximate computing techniques for accelerating compute intensive workloads
… and performance for a learning problem. KMeans, DBSCAN, etc. are a few choices for clustering algorithms and the ideal choice of the algorithm is input or architecture dependent. An algorithm that is designed for one kind of model will generally fail on a data set that contains a radically …
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