Please use this identifier to cite or link to this item:
http://hdl.handle.net/11375/27385
Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.advisor | McNicholas, Sharon | - |
dc.contributor.advisor | Jeganathan, Pratheepa | - |
dc.contributor.author | Xu, Jini | - |
dc.date.accessioned | 2022-02-24T19:59:08Z | - |
dc.date.available | 2022-02-24T19:59:08Z | - |
dc.date.issued | 2022 | - |
dc.identifier.uri | http://hdl.handle.net/11375/27385 | - |
dc.description.abstract | Clustering helps in understanding the natural grouping and internal structure of data. Model-based clustering considers each cluster as a component in a mixture model. As the data dimensionality and complexity increase, model-based clustering tends to over-parametrize results. Thus, it is important to select a subset of critical variables instead of using all the variables for clustering. This study considers two variable selection methods for model-based clustering on real world high-dimensional data; variable selection for clustering and classification (VSCC) and variable selection for model-based clustering (clustvarsel). For simplicity, Gaussian mixture models were applied. Three criteria are used to compare the clustering accuracy and efficiency, which are the adjusted rand index (ARI), mis-clustering error, and performance time (in seconds). | en_US |
dc.language.iso | en | en_US |
dc.subject | Clustering | en_US |
dc.subject | Statistics | en_US |
dc.title | Variable Selection Methods for Model-based Clustering and Application to High-dimensional 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 | Master of Science (MSc) | en_US |
Appears in Collections: | Open Access Dissertations and Theses |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
Xu_Jini_finalsubmission202202_MSc.pdf | 8.45 MB | Adobe PDF | View/Open | |
Jini Xu_final_submission_sheet.pdf | Final Thesis Submission Sheet | 183.81 kB | Adobe PDF | View/Open |
Jini Xu_License to McMaster Form.pdf | McMaster University Licence | 90.45 kB | Adobe PDF | View/Open |
Items in MacSphere are protected by copyright, with all rights reserved, unless otherwise indicated.