- Library Home /
- Search Collections /
- Open Collections /
- Browse Collections /
- UBC Theses and Dissertations /
- Enhanced k-prototypes clustering for mixed data by...
Open Collections
UBC Theses and Dissertations
UBC Theses and Dissertations
Enhanced k-prototypes clustering for mixed data by bootstrap augmentation Chang, Fujia
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.
Item Metadata
| Title |
Enhanced k-prototypes clustering for mixed data by bootstrap augmentation
|
| Creator | |
| Supervisor | |
| Publisher |
University of British Columbia
|
| Date Issued |
2024
|
| Description |
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.
|
| Genre | |
| Type | |
| Language |
eng
|
| Date Available |
2024-10-03
|
| Provider |
Vancouver : University of British Columbia Library
|
| Rights |
Attribution-NonCommercial-NoDerivatives 4.0 International
|
| DOI |
10.14288/1.0445486
|
| URI | |
| Degree | |
| Program | |
| Affiliation | |
| Degree Grantor |
University of British Columbia
|
| Graduation Date |
2024-11
|
| Campus | |
| Scholarly Level |
Graduate
|
| Rights URI | |
| Aggregated Source Repository |
DSpace
|
Item Media
Item Citations and Data
Rights
Attribution-NonCommercial-NoDerivatives 4.0 International