Degree Name
MS (Master of Science)
Program
Mathematical Sciences
Date of Award
12-2018
Committee Chair or Co-Chairs
JeanMarie Hendrickson
Committee Members
Robert Price, Nicole Lewis
Abstract
Clustering is a widely used technique in data mining applications to source, manage, analyze and extract vital information from large amounts of data. Most clustering procedures are limited in their performance when it comes to data with mixed attributes. In recent times, mixed data have evolved to include directional and functional data. In this study, we will give an introduction to clustering with an eye towards the application of the extended Gower coefficient by Hendrickson (2014). We will conduct a simulation study to assess the performance of this coefficient on mixed data whose functional component has strictly-decreasing signal curves and also those whose functional component has a mixture of strictly-decreasing signal curves and periodic tendencies. We will assess how four different hierarchical clustering algorithms perform on mixed data simulated under varying conditions with and without weights. The comparison of the various clustering solutions will be done using the Rand Index.
Document Type
Thesis - unrestricted
Recommended Citation
Koomson, Obed, "Performance Assessment of The Extended Gower Coefficient on Mixed Data with Varying Types of Functional Data." (2018). Electronic Theses and Dissertations. Paper 3512. https://dc.etsu.edu/etd/3512
Copyright
Copyright by the authors.