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dc.contributor.advisorNezamoddini-Kachouie, Nezamoddin
dc.contributor.authorShutaywi, Meshal 2019
dc.descriptionThesis (Ph.D.) - Florida Institute of Technology, 2019.en_US
dc.description.abstractDue to advancements in data acquisition, large amount of data are collected on a daily basis. Analysis of the collected data is an important task to discover the patterns, extract the features, and make informed decisions. A vital step in data analysis is dividing the subjects (elements, individuals) in different groups based on their similarities. One way to group the subjects is clustering. Clustering methods can be divided into two categories, linear and non-linear. K-means is a commonly used linear clustering method, while Kernel K-means is a non-linear technique. Kernel K-means projects the elements to a new space using a kernel function and then clusters them in different groups. Different kernels perform differently when they are applied to different data sets and as a result choosing the right kernel for an application could be challenging. Therefore, applying a set of kernels and aggregating the results could provide a robust performance for different data sets. In this work, we address this issue and propose a weighted majority voting to ensemble the results obtained by different kernels.en_US
dc.rightsCC BY 4.0en_US
dc.titleWeighted Aggregation Methods for Linear and Nonlinear Cluster Analysis with Applications to Cancer Researchen_US
dc.typeDissertationen_US of Philosophy in Operations Researchen_US Researchen_US Sciencesen_US Institute of Technologyen_US

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Except where otherwise noted, this item's license is described as CC BY 4.0