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.
","abstract_html":"<p>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.</p>","abstract_has_math":false,"creators":["Cloyd, James Dale"],"institution":null,"degree_name":"MS (Master of Science)","degree_level":"Thesis - unrestricted","degree_discipline":"Computer and Information Science","degree_department":null,"school":null,"contributors":[],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2002,"date_issued":"2002-12-01T08:00:00Z","date_published":"2002-12-01T08:00:00Z","updated_at":"2026-07-24T02:19:21Z","subjects":["evolutionary operations","knowledge discovery in databases","neural networks","simplex evop","maximum likelihood estimation","non-linear regression","robust regression","Genetic algorithms","Computer Sciences","Physical Sciences and Mathematics"],"languages":[],"rights":["Copyright by the authors."],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://dc.etsu.edu/etd/714","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:creator","label":"Author","values":["Cloyd, James Dale"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.available","label":"Dc Date Available","values":["1990-01-01T08:00:00Z"]},{"key":"dc:date.issued","label":"Date","values":["2002-12-01T08:00:00Z"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Computer and Information Science"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis - unrestricted"]},{"key":"thesis:degree_name","label":"Degree Name","values":["MS (Master of Science)"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["evolutionary operations","knowledge discovery in databases","neural networks","simplex evop","maximum likelihood estimation","non-linear regression","robust regression","Genetic algorithms","Computer Sciences","Physical Sciences and Mathematics"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:rights","label":"Dc Rights","values":["Copyright by the authors."]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://dc.etsu.edu/context/etd/article/1871/viewcontent/CloydJ111302a.pdf","https://dc.etsu.edu/etd/714"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["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.
"]},{"key":"dc:title","label":"Title","values":["Data Mining with Newton's Method."]}]}],"canonical_facts":{"dc:creator":["Cloyd, James Dale"],"dc:date.available":["1990-01-01T08:00:00Z"],"dc:date.issued":["2002-12-01T08:00:00Z"],"dc:description.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.
"],"dc:identifier":["https://dc.etsu.edu/context/etd/article/1871/viewcontent/CloydJ111302a.pdf","https://dc.etsu.edu/etd/714"],"dc:rights":["Copyright by the authors."],"dc:subject":["evolutionary operations","knowledge discovery in databases","neural networks","simplex evop","maximum likelihood estimation","non-linear regression","robust regression","Genetic algorithms","Computer Sciences","Physical Sciences and Mathematics"],"dc:title":["Data Mining with Newton's Method."],"thesis:degree_discipline":["Computer and Information Science"],"thesis:degree_level":["Thesis - unrestricted"],"thesis:degree_name":["MS (Master of Science)"]},"updated_at":"2026-07-24T02:19:21Z"}