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http://hdl.handle.net/11375/28188
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DC Field | Value | Language |
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dc.contributor.advisor | McNicholas, Paul D | - |
dc.contributor.author | Alamer, Eman Mohammed S | - |
dc.date.accessioned | 2023-01-03T14:36:14Z | - |
dc.date.available | 2023-01-03T14:36:14Z | - |
dc.date.issued | 2022 | - |
dc.identifier.uri | http://hdl.handle.net/11375/28188 | - |
dc.description.abstract | Clustering, also known as unsupervised classification, is a foundational machine learning technique and is used to find underlying group structures in data. There are many well-established model-based techniques to analyze either categorical or continuous data in the clustering paradigm. However, there is a relative paucity of work for mixed-type data, especially mixed data where the continuous variables exhibit skewness and heavy tails. In this thesis, different methodologies and models are presented for analyzing asymmetric and mixed-typed data. The first method is a mixture model for analyzing asymmetric mixed-type data. The second is modelling contaminated mixed-type data and identifying potential outliers. Lastly, model averaging techniques are developed for skewed-data based on Occam’s window and parsimonious mixture models. The expectation-maximization algorithm is used here to estimate the model parameters. Both real and simulated data are used for illustration. | en_US |
dc.language.iso | en_US | en_US |
dc.title | Unsupervised Classification for Skewed and Mixed-Type Data | en_US |
dc.type | Thesis | en_US |
dc.contributor.department | Mathematics and Statistics | en_US |
dc.description.degreetype | Thesis | en_US |
dc.description.degree | Doctor of Science (PhD) | en_US |
Appears in Collections: | Open Access Dissertations and Theses |
Files in This Item:
File | Description | Size | Format | |
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Alamer_Eman_M.S._202212_PhD.pdf | 7.16 MB | Adobe PDF | View/Open |
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