Clustering is the process of finding underlying group structure in data. As the scale of
data collection continues to grow, this “big data” phenomenon results in more complex data structures. These data structures are not always compatible with traditional
clustering methods, making their use problematic. This thesis presents methodology
for analyzing samples of four-way and higher data, examples of these more complex
data types. These data structures consist of samples of continuous data arranged in
multidimensional arrays. A large emphasis is placed on clustering this data using
mixture models that leverage tensor-variate distributions to model the data. Parameter estimation for all these methods are based on the expectation-maximization
algorithm. Both simulated and real data are used for illustration. / Thesis / Doctor of Science (PhD)
| Identifer | oai:union.ndltd.org:mcmaster.ca/oai:macsphere.mcmaster.ca:11375/26653 |
| Date | January 2021 |
| Creators | Tait, Peter A. |
| Contributors | McNicholas, Paul D., Statistics |
| Source Sets | McMaster University |
| Language | English |
| Detected Language | English |
| Type | Thesis |
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