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). / Thesis / Master of Science (MSc)
| Identifer | oai:union.ndltd.org:mcmaster.ca/oai:macsphere.mcmaster.ca:11375/27385 |
| Date | January 2022 |
| Creators | Xu, Jini |
| Contributors | McNicholas, Sharon, Jeganathan, Pratheepa, Mathematics and Statistics |
| Source Sets | McMaster University |
| Language | English |
| Detected Language | English |
| Type | Thesis |
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