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 11 of 11 for “"affinity propagation"”.
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Application of maximal information coefficient and affinity propagation to characterizing seismic time series associated with earthquakes
… of the feature dataset generated by HCTSA. Affinity propagation (AP) was used for clustering similar features and selecting exemplary features from different clusters. These independent exemplary features were determined to characterize the original data. The process was applied to data from …
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A novel computational method for inferring dynamic genetic regulatory trajectories
We present a novel method called Time Series Affinity Propagation (TSAP) for inferring regulatory states and trajectories from time series genomic data. This method builds on the Affinity Propagation method of Frey and Dueck [10]. TSAP incorporates temporal constraints to more accurately model the …
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Malware Recognition by Properties of Executables
This thesis explores what patterns, if any, exist to differentiate non-malware from malware, given only a sequence of raw bytes composing either a received file or a fixed-length initial segment of a received file. If any such patterns are found, their effectiveness as filtering criteria is …
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Detecting high level story elements from low level data
… in transition space. I analyze, then select the affinity propagation clustering algorithm to group the events using only their low-level representations. To this end, I present a novel algorithm for determining how similar any two points in transition space are. Due to the lack of vision systems …
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Metabolic Diversity in Human Non-Small Cell Lung Cancer Cells
… cell lines grown under identical conditions. Affinity propagation clustering using metabolic features alone produced families that were largely distinct from clusters based solely on gene expression. Nevertheless, databases of metabolic features and orthogonal data sets could be cross-queried …
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Encouraging Inactive Users towards Effective Recommendation
… connected subgraphs (HCS), Markov clustering and Affinity Propagation (AP) clustering were explored in this thesis to check if they have the capabilities to achieve these required outputs. The suitable clustering technique amongst these techniques that is able to identify exemplars in each cluster …
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Real time detection of malicious webpages using machine learning techniques
… unsupervised techniques are Self-Organising Map, Affinity Propagation and K-Means. Self-Organising Map was used instead of Neural Networks and the research suggests that the new version of Neural Network i.e. Deep Learning would be great for this research. The supervised algorithms performed …
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Analysis and Error Correction in Structures of Macromolecular Interiors and Interfaces
… complicate hand-curation. I demonstrate that affinity propagation successfully differentiates between two related but distinct suite conformers, and is a useful tool for automated conformer clustering. </p><p>My study of protein sidechain rotamers in X-ray structures identifies a class of …
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Dimensionality Reduction, Feature Selection and Visualization of Biological Data
… into a new distance metric, we apply affinity propagation clustering (APC) to build gene sub-networks; secondly, we further incorporate functional gene sets knowledge to complement the physical interaction information; finally, based on the constructed sub-network and gene set …
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Characterisation of Copy Number Changes in the Progression of Barrett’s Oesophagus
… in the progressor patients when analysed using affinity propagation clustering. These data allowed us to develop a regression model to predict progression. Using the GLM model, we successfully classified samples as early as progressor or not with an AUC of 85.75% and a sensitivity and …
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Outcome prediction and structure discovery in healthcare data
Growing use of electronic medical records, advances in data mining and machine learning, and the continually increasing cost of healthcare in the United States drive the necessity of algorithmic solutions with the potential to improve patient care and reduce healthcare costs. Such algorithms can …