I introduce a novel recursive modification to the classical Goemans-Williamson MaxCut algorithm, offering improved performance in vectorized data clustering tasks. Focusing on the clustering of medical publications, I suggest to employ recursive iterations in conjunction with a dimension relaxation method to enhance density of clustering results. Furthermore, I propose a new vectorization technique for articles, leveraging conditional probabilities for more effective clustering. I believe that these methods will provide advantages in both computational efficiency and clustering accuracy. I will analyze the effectiveness of recursive iterations and higher-dimensional generalizations of the GWA in the hopes of achieving more accurate dissimilarity-based clustering. I think these methods combined with dimensionality reduction have the potential to further enhance clustering results. In addition, the introduction of the vectorization method based on conditional probabilities will provide an additional tool for unsupervised document classification. While GWA shows promise in accurately clustering articles, there are some challenges that will need to be researched and refined on other collected or computer-generated datasets before being applied. Future development of techniques to handle outliers and to fine-tune the parameters will contribute to a more precise and robust method.
","abstract_html":"<p>I introduce a novel recursive modification to the classical Goemans-Williamson MaxCut algorithm, offering improved performance in vectorized data clustering tasks. Focusing on the clustering of medical publications, I suggest to employ recursive iterations in conjunction with a dimension relaxation method to enhance density of clustering results. Furthermore, I propose a new vectorization technique for articles, leveraging conditional probabilities for more effective clustering. I believe that these methods will provide advantages in both computational efficiency and clustering accuracy. I will analyze the effectiveness of recursive iterations and higher-dimensional generalizations of the GWA in the hopes of achieving more accurate dissimilarity-based clustering. I think these methods combined with dimensionality reduction have the potential to further enhance clustering results. In addition, the introduction of the vectorization method based on conditional probabilities will provide an additional tool for unsupervised document classification. While GWA shows promise in accurately clustering articles, there are some challenges that will need to be researched and refined on other collected or computer-generated datasets before being applied. Future development of techniques to handle outliers and to fine-tune the parameters will contribute to a more precise and robust method.</p>","abstract_has_math":false,"creators":["Ly, An"],"institution":null,"degree_name":"Mathematics, PhD","degree_level":"Open Access Dissertation","degree_discipline":"Institute of Mathematical Sciences","degree_department":null,"school":null,"contributors":["Ali Nadim","Qidi Peng"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2024,"date_issued":"2024-01-01T08:00:00Z","date_published":"2024-01-01T08:00:00Z","updated_at":"2026-07-24T01:41:01Z","subjects":["Max kCut Optimization","Text Classification","Mathematics"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://scholarship.claremont.edu/cgu_etd/900","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Ali Nadim","Qidi Peng"]},{"key":"dc:creator","label":"Author","values":["Ly, An"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.available","label":"Dc Date Available","values":["2025-01-09T08:00:00Z"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Institute of Mathematical Sciences"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Open Access Dissertation"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Mathematics, PhD"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Max kCut Optimization","Text Classification","Mathematics"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://scholarship.claremont.edu/cgu_etd/900"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["I introduce a novel recursive modification to the classical Goemans-Williamson MaxCut algorithm, offering improved performance in vectorized data clustering tasks. Focusing on the clustering of medical publications, I suggest to employ recursive iterations in conjunction with a dimension relaxation method to enhance density of clustering results. Furthermore, I propose a new vectorization technique for articles, leveraging conditional probabilities for more effective clustering. I believe that these methods will provide advantages in both computational efficiency and clustering accuracy. I will analyze the effectiveness of recursive iterations and higher-dimensional generalizations of the GWA in the hopes of achieving more accurate dissimilarity-based clustering. I think these methods combined with dimensionality reduction have the potential to further enhance clustering results. In addition, the introduction of the vectorization method based on conditional probabilities will provide an additional tool for unsupervised document classification. While GWA shows promise in accurately clustering articles, there are some challenges that will need to be researched and refined on other collected or computer-generated datasets before being applied. Future development of techniques to handle outliers and to fine-tune the parameters will contribute to a more precise and robust method.
"]},{"key":"dc:title","label":"Title","values":["Applications and Analysis of NLP Deep Learning Models for Antibiotic's Side Effects Classifications"]}]}],"canonical_facts":{"dc:contributor":["Ali Nadim","Qidi Peng"],"dc:creator":["Ly, An"],"dc:date.available":["2025-01-09T08:00:00Z"],"dc:description.abstract":["I introduce a novel recursive modification to the classical Goemans-Williamson MaxCut algorithm, offering improved performance in vectorized data clustering tasks. Focusing on the clustering of medical publications, I suggest to employ recursive iterations in conjunction with a dimension relaxation method to enhance density of clustering results. Furthermore, I propose a new vectorization technique for articles, leveraging conditional probabilities for more effective clustering. I believe that these methods will provide advantages in both computational efficiency and clustering accuracy. I will analyze the effectiveness of recursive iterations and higher-dimensional generalizations of the GWA in the hopes of achieving more accurate dissimilarity-based clustering. I think these methods combined with dimensionality reduction have the potential to further enhance clustering results. In addition, the introduction of the vectorization method based on conditional probabilities will provide an additional tool for unsupervised document classification. While GWA shows promise in accurately clustering articles, there are some challenges that will need to be researched and refined on other collected or computer-generated datasets before being applied. Future development of techniques to handle outliers and to fine-tune the parameters will contribute to a more precise and robust method.
"],"dc:identifier":["https://scholarship.claremont.edu/cgu_etd/900"],"dc:subject":["Max kCut Optimization","Text Classification","Mathematics"],"dc:title":["Applications and Analysis of NLP Deep Learning Models for Antibiotic's Side Effects Classifications"],"thesis:degree_discipline":["Institute of Mathematical Sciences"],"thesis:degree_level":["Open Access Dissertation"],"thesis:degree_name":["Mathematics, PhD"]},"updated_at":"2026-07-24T01:41:01Z"}