Thesis Defence: Enhanced K-Prototypes Clustering for Mixed Data by Bootstrap Augmentation

Fujia Chang, supervised by Dr. Jeffrey Andrews, will defend their thesis titled “Enhanced K-Prototypes Clustering for Mixed Data by Bootstrap Augmentation” in partial fulfillment of the requirements for the degree of Master of Science in Mathematics.

An abstract for Fujia Chang’s thesis is included below.

Defences are open to all members of the campus community as well as the general public. Please email jeff.andrews@ubc.ca to receive the Zoom link for this defence.

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ABSTRACT

The k-prototypes algorithm is a popular approach for clustering mixed data, yet it faces challenges such as susceptibility to local optima and misclassification of boundary observations with no measure of uncertainty due to hard partitioning. Our proposal integrates bootstrap-augmented optimization with k-prototypes to address these issues: expanding the search space of the algorithm while simultaneously providing probabilistic estimates for cluster memberships. We demonstrate the utility of this approach through simulations and real-world data analyses.