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 158 for “"Clustering algorithms"”.
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Neuroengineering of Clustering Algorithms
… divided into multivariate data visualization, clustering algorithms, and cluster validation. This dissertation contributes neural network-based techniques to perform all three unsupervised learning tasks. Particularly, the first paper provides a comprehensive review on adaptive resonance theory …
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SPHERICAL AND STOCHASTIC CO-CLUSTERING ALGORITHMS
Clustering, without a doubt, is a dominating area in data mining and machine learning field. Due to the wide range of the necessity to clustering algorithms, it has many applications in real-life problems, ranging from bioinformatics to personalized information delivery. Feature characteristics of …
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Optimizing clustering algorithms for computer vision
Thesis (M.Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2001.
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Private k-means clustering : algorithms and applications
… practical system applying differential privacy algorithms for clustering points on real databases. This thesis describes the construction of small coresets for computing k-means clustering of a set of points while preserving differential privacy. As a result, it gives the first 𝑘-means …
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Monte Carlo validation of two genetic clustering algorithms
… two newly developed genetic cluster analysis algorithms, GENCLUS and GENCLUS+, were validated by comparing their performance against that of three popular clustering techniques (Ward's method, K-means w/ random seeds, K-means w/Ward's centroids) and in an elaborate Monte Carlo study. …
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A Performance Comparison Of Clustering Algorithms In Ad Hocnetworks
… performance comparison of four ad hoc network clustering protocols: Dynamic Mobile Adaptive Clustering (DMAC), Highest-Degree and Lowest-ID algorithms, and Weighted Clustering Algorithm (WCA). Yet Another Extensible Simulation (YAES) was used as the simulator to carry out the simulations.
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Clustering Algorithms for Component Placement in Printed Circuit Boards
… we integrated the Louvain and Leiden clustering algorithms for component clustering in PCB placement. We also showed comparative metrics with the spectral clustering algorithm applied to unweighted graph representations, which is the prior state of this project, but it has no knowledge …
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Clustering algorithms to further enhance predictable situational data in vehicular ad-hoc networks
… This thesis explores different ways of clustering local groups of vehicles along with machine learning algorithms to predict where vehicles are likely to be and detect false or impossible information.
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Clustering algorithms for sensor networks and mobile ad hoc networks to improve energy efficiency
Many clustering algorithms have been proposed to improve energy efficiency of ad hoc networks as this is one primary challenge in ad hoc networks. The design of these clustering algorithms in sensor networks is different from that in mobile ad hoc networks in accordance with their specific …
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Parallel Algorithms, Optimizations, and Benchmarks for Metric and Graph Clustering
Clustering is a fundamental unsupervised machine learning task of detecting groups of similar objects in data. Clustering can be used to identify the underlying substructures of data and can detect essential functional groups, such as people with similar interests, news articles on similar topics, …
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Optimal clustering techniques for metagenomic sequencing data
… 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 performed an extensive comparison of six commonly-used clustering algorithms …
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Data mining and database systems: integrating conceptual clustering with a relational database management system.
Many clustering algorithms have been developed and improved over the years to cater for large scale data clustering. However, much of this work has been in developing numeric based algorithms that use efficient summarisations to scale to large data sets. There is a growing need for scalable …
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Schemas of Clustering
Data mining techniques, such as clustering, have become a mainstay in many applications such as bioinformatics, geographic information systems, and marketing. Over the last decade, due to new demands posed by these applications, clustering techniques have been significantly adapted and extended. …
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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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Pattern extraction and clustering for high-dimensional discrete data
… Our goal is to develop effective approximation algorithms with good theoretical properties and apply them to solve various real application problems. We reformulate each of the problems as a special clustering problem that has the same optimal solution as the corresponding original problem. …
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