Degree Name
MS (Master of Science)
Program
Computer and Information Science
Date of Award
12-2002
Committee Chair or Co-Chairs
Donald B. Sanderson
Committee Members
Kellie Price, Lorie Moffitt
Abstract
Capable and well-organized data mining algorithms are essential and fundamental to helpful, useful, and successful knowledge discovery in databases. We discuss several data mining algorithms including genetic algorithms (GAs). In addition, we propose a modified multivariate Newton's method (NM) approach to data mining of technical data. Several strategies are employed to stabilize Newton's method to pathological function behavior. NM is compared to GAs and to the simplex evolutionary operation algorithm (EVOP). We find that GAs, NM, and EVOP all perform efficiently for well-behaved global optimization functions with NM providing an exponential improvement in convergence rate. For local optimization problems, we find that GAs and EVOP do not provide the desired convergence rate, accuracy, or precision compared to NM for technical data. We find that GAs are favored for their simplicity while NM would be favored for its performance.
Document Type
Thesis - unrestricted
Recommended Citation
Cloyd, James Dale, "Data Mining with Newton's Method." (2002). Electronic Theses and Dissertations. Paper 714. https://dc.etsu.edu/etd/714
Copyright
Copyright by the authors.