Cluster analysis is used to detect underlying group structure in data. Model-based
clustering is the process of performing cluster analysis which involves the fitting of
finite mixture models. However, parameter estimation in mixture model-based approaches
to clustering is notoriously difficult. To this end, this thesis focuses on the
development of evolutionary computation as an alternative technique for parameter
estimation in mixture models. An evolutionary algorithm is proposed and illustrated
on the well-established Gaussian mixture model with missing values. Next, the family
of Gaussian parsimonious clustering models is considered, and an evolutionary
algorithm is developed to estimate the parameters. Next, an evolutionary algorithm
is developed for latent Gaussian mixture models and to facilitate the flexible clustering
of high-dimensional data. For all models and families of models considered in
this thesis, the proposed algorithms used for model-fitting and parameter estimation
are presented and the performance illustrated using real and simulated data sets to
assess the clustering ability of all models. This thesis concludes with a discussion
and suggestions for future work. / Dissertation / Doctor of Philosophy (PhD)
| Identifer | oai:union.ndltd.org:mcmaster.ca/oai:macsphere.mcmaster.ca:11375/26960 |
| Date | January 2021 |
| Creators | Kampo, Regina S. |
| Contributors | McNicholas, Paul D., McNicholas, Sharon M., Statistics |
| Source Sets | McMaster University |
| Language | English |
| Detected Language | English |
| Type | Thesis |
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