Date of Award
Campus Access Thesis
Epidemiology and Biostatistics
This thesis focuses on clustering fifteen Zernike coefficients using the method of clustering of linear regression models (CLM). EM algorithm is used to infer the maximum likelihood estimate of parameters for each cluster. Bayesian information criterion (BIC) combined with Bootstrapped maximum volume (BMV) criterion are used to determine the number of clusters. The Bootstrap method is used to estimate the uncertainty on the number of clusters. These fifteen Zernike coefficients are clustered into four clusters with a 90% confidence interval of the number of clusters being (2, 5).
Bao, W.(2010). Clustering Analysis of Zernike Coefficients From High Order Aberration Patients. (Master's thesis). Retrieved from https://scholarcommons.sc.edu/etd/134