Search for dissertations about: "DISSERTATIONS ON CLUSTERING"
Showing result 1 - 5 of 92 swedish dissertations containing the words DISSERTATIONS ON CLUSTERING.
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1. Incremental Clustering of Source Code : a Machine Learning Approach
Abstract : Technical debt at the architectural level is a severe threat to software development projects. Uncontrolled technical debt that is allowed to accumulate will undoubtedly hinder speedy development and maintenance, introduce bugs and problems in the software product, and may ultimately result in the abandonment of the source code. READ MORE
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2. Essays on Distance Based (Non-Euclidean) Tests for Spatial Clustering in Inhomogeneous Populations : Adjusting for the Inhomogeneity through the Distance Used
Abstract : This thesis consits of four papers dealing with distance based (non-Euclidean) tests for spatial clustering in inhomogeneous populations. The density adjusted distance (DAD), which considers the underlying density, is defined in the first paper. READ MORE
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3. Consensus Algorithms for Trees and Strings
Abstract : This thesis studies the computational complexity and polynomial-time approximability of a number of discrete combinatorial optimization problems involving labeled trees and strings. The problems considered have applications to computational molecular biology, pattern matching, and many other areas of computer science. READ MORE
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4. On the Size and Shape of Polymers and Polymer Complexes : A Computational and Light Scattering Study
Abstract : Detailed characterization of size and shape of polymers, and development of methods to elucidate the mechanisms behind shape transitions are central issues in this thesis. In particular we characterize grafted polymer chains under confinement in terms of the chain entanglement complexity and mean molecular size. READ MORE
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5. Frames of threat and solidarity : Dynamics of media discourse on immigration in Sweden
Abstract : This dissertation aims to analyse media discourse about immigration in Sweden in the last decade. To meet this goal, it uses large-scale textual data collected from various media resources, such as mainstream newspapers, social media (Twitter and Facebook) and an online forum. READ MORE