MS (Master of Science)
Date of Award
Committee Chair or Co-Chairs
JeanMarie L Hendrickson
Michele L. Joyner, Robert M. Price
Clustering is an important analytical technique that has proven to affect human life positively through its application in cancer research, market segmentation, city planning etc. In this time of growing technological systems, mixed data has seen another face of longitudinal, directional and functional attributes which is worth paying attention to and analyzing. Previous research works on clustering relied largely on the inverse weight technique and B-spline in smoothing data and assessing the performance of various clustering algorithms. In 1971, Gower proposed a method of clustering for mixed variable types which has been extended to include functional and directional variables by Hendrickson (2014). In this study, we will do a comparative analysis of the performance of the hierarchical clustering mechanism using a simulated Functional data with mixed structure. We will adopt the Fourier basis smoothing procedure and use the Rand index (Rand 1971) and adjusted Rand index for the comparison of the various clustering algorithms.
Thesis - unrestricted
Amartey, Ishmael, "Functional Mixed Data Clustering with Fourier Basis Smoothing" (2021). Electronic Theses and Dissertations. Paper 3996. https://dc.etsu.edu/etd/3996
Copyright by the authors.