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 7 of 7 for “"Multivariate Clustering"”.
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Multivariate clustering of chronic pain patients : a replication using the MMPI-2
This study addresses the problem of assessment of chronic pain patients, a population with special needs which have only begun to be recognized by the medical community. While this paper promotes a comprehensive approach to assessment and treatment of chronic pain patients, the research questions …
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The application of univariate and distributional analyses to assess the impacts of diamond mining on marine macrofauna off the Namibian Coast
… the coast of Namibia. The first study dealt with multivariate clustering analysis of the first samples before and after mining. The second study focused on recovery times after mining and this study is aimed at estimating the amount of stress encountered by benthic communities, for comparision …
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Time series data analytics : clustering-based anomaly detection techniques for quality control in semiconductor manufacturing
… more thorough analysis of the data is needed, a multivariate clustering analysis can also be computed. In addition, decomposition-based algorithms are presented. These rely on techniques such as the STL and SAX representations of time series, and provide a visual computation of time series …
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On neural spike sorting with mixture models
… can not be formulated in standard terms of multivariate clustering. Because a spike can originate from simultaneous activity of multiple neurons, and is called an overlapped spike. These overlapped spikes do not belong to any of the available clusters. Therefore new model can be developed. …
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A framework for Improving Hydrologic and Water Quality Prediction in Urbanized Watersheds through Stakeholder Co-Design and Multi-Model Integration
… parameterization, followed by univariate and multivariate clustering to identify data-driven hydro-climatic patterns. Insights from these analyses informed the development of a hybrid dynamic parameterization in which model parameters varied continuously with time and varied with respect to …
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Statistical Machine Learning for Multi-platform Biomedical Data Analysis
… This method has combined the advantages of multivariate clustering, convex optimization and compartmental modeling approaches. Interactions among genetic loci are believed to play an important role in disease risk. Due to the huge dimension of SNP data (normally several millions in …
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Modelling British Columbia’s ecosystems and avian richness using landscape-scale indirect indicators of biodiversity
… indicators of biodiversity using a two-step clustering algorithm. The results display 16 ecologically distinct terrestrial ecosystems, 10 of which characterize the northern Boreal, coastal and Southern Interior mountain regions, and six represent the coastal lowlands, interior, Georgia …