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 “"k-medoids"”.
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Visualisation of Mental Health Community Service Patient Pathways
… 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 syntactical …
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On Sequence Clustering and Supervised Dimensionality Reduction
… classical clustering algorithms, including the k-medoids algorithm and hierarchical agglomerative clustering (HAC) algorithms. Data sequences are generated from unknown continuous distributions that are assumed to form clusters according to some well-defined distance metrics. The goal is to group …
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A Computer Vision and Generative AI Framework for Trend Extraction and Brand-Aligned Fashion Design
… model, colour palettes extracted using K-Medoids clustering, and fabric patterns classified using a pretrained ResNet34 model. The predictions are then fed into the trend identification module, which uses a self-organising map to identify trends. The trends, a secondary fabric dataset, and …
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Task-Aware Spatial and Temporal Aggregation for Capacity Expansion Planning
… behavior, and spatial correlation, and uses k-medoids clustering to define spatial zones. Temporal aggregation is then applied to daily system-wide profiles, selecting representative days that maintain cross-zonal interactions. The result is a reduced spatio-temporal dataset fed into a CEP …
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A Distance-Based Clustering Framework for Categorical Time Series: A Case Study in Episodes of Care Healthcare Delivery System
… is unknown. The proposed framework utilizes k-medoids clustering as it accommodates string-based distance measures commonly used by categorical sequences while meeting the expectations of physicians that the method returns an existing pathway as the central point and produces clusters of …
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Modular synthesis for cooperative small molecule ligands and chemical education
… techniques such as K-nearest neighbors, K-medoids clustering, modularization of target molecules to identify repeating bond-types, and optimization of molecular function by identifying high-performing building blocks. Students also get hands-on experience in the power of modular synthesis …
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Analyzing Freight Congestion and Transportation Performance Measures Using the National Performance Management Research Data Set (NPMRDS)
… classic clustering algorithms (K-means, K-medoids, Hierarchical, and DB-Scan) and selecting the optimal method to apply in each county. Phase VI studies how travel speed is affected by different weather types. A significant unsupervised machine learning method, Self-Organizing Maps (SOMs), …
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A pragmatic approach to multi-objective optimisation for portfolio asset management
… of the a posteriori method, the K-means and K-medoids methods were applied to pruning Pareto optimal solutions obtained from a selected MOEA. We also presented two novel indicators based on average Euclidean distance and cosine similarity metrics. The application of these indicators not only …
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Semantic Frameworks for Document and Ontology Clustering
… document clustering algorithms K-Means and K-Medoids. In addition, it overcomes a major drawback of K-Means/Medoids algorithms in that the number of clusters can be dynamically determined by splitting and merging clusters. Fuzzy clustering with this approach has also been investigated. The …
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Identifying Asymmetries in Web-based Transfer Student Information that is Believed to be Correct using Fully Integrated Mixed Methods
… and language simultaneously. K-means and K-medoids cluster methods both produced the same four cluster solution illustrating one aspect of information asymmetries through fragmentation. The clustering solution highlighted four major network patterns, plus one cluster mixing two of the …