Please use this identifier to cite or link to this item:
http://hdl.handle.net/11375/28188
Title: | Unsupervised Classification for Skewed and Mixed-Type Data |
Authors: | Alamer, Eman Mohammed S |
Advisor: | McNicholas, Paul D |
Department: | Mathematics and Statistics |
Publication Date: | 2022 |
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. |
URI: | http://hdl.handle.net/11375/28188 |
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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