{"id":{"repo_id":"uno","oai_identifier":"oai:scholarworks.uno.edu:td-1430"},"canonical_url":"https://search.dev.ndltd.org/etd/uno/oai:scholarworks.uno.edu:td-1430","repository":{"repo_id":"uno","name":"University of New Orleans","base_url":"https://scholarworks.uno.edu/do/oai/"},"display":{"title":"External Support Vector Machine Clustering","abstract":"The external-Support Vector Machine (SVM) clustering algorithm clusters data vectors with no a priori knowledge of each vector's class. The algorithm works by first running a binary SVM against a data set, with each vector in the set randomly labeled, until the SVM converges. It then relabels data points that are mislabeled and a large distance from the SVM hyperplane. The SVM is then iteratively rerun followed by more label swapping until no more progress can be made. After this process, a high percentage of the previously unknown class labels of the data set will be known. With sub-cluster identification upon iterating the overall algorithm on the positive and negative clusters identified (until the clusters are no longer separable into sub-clusters), this method provides a way to cluster data sets without prior knowledge of the data's clustering characteristics, or the number of clusters.","abstract_html":"The external-Support Vector Machine (SVM) clustering algorithm clusters data vectors with no a priori knowledge of each vector's class. The algorithm works by first running a binary SVM against a data set, with each vector in the set randomly labeled, until the SVM converges. It then relabels data points that are mislabeled and a large distance from the SVM hyperplane. The SVM is then iteratively rerun followed by more label swapping until no more progress can be made. After this process, a high percentage of the previously unknown class labels of the data set will be known. With sub-cluster identification upon iterating the overall algorithm on the positive and negative clusters identified (until the clusters are no longer separable into sub-clusters), this method provides a way to cluster data sets without prior knowledge of the data's clustering characteristics, or the number of clusters.","abstract_has_math":false,"creators":["McChesney, Charlie"],"institution":null,"degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Computer Science","degree_department":null,"school":null,"contributors":["Winters-Hilt, Stephen","Fu, Bin","Tu, Shengru"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2006,"date_issued":"2006-08-09T07:00:00Z","date_published":"2006-08-09T07:00:00Z","updated_at":"2026-07-24T05:28:22Z","subjects":[],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://scholarworks.uno.edu/td/409","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Winters-Hilt, Stephen","Fu, Bin","Tu, Shengru"]},{"key":"dc:creator","label":"Author","values":["McChesney, Charlie"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"thesis:degree_discipline","label":"Discipline","values":["Computer Science"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["M.S."]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://scholarworks.uno.edu/td/409"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["The external-Support Vector Machine (SVM) clustering algorithm clusters data vectors with no a priori knowledge of each vector's class. The algorithm works by first running a binary SVM against a data set, with each vector in the set randomly labeled, until the SVM converges. It then relabels data points that are mislabeled and a large distance from the SVM hyperplane. The SVM is then iteratively rerun followed by more label swapping until no more progress can be made. After this process, a high percentage of the previously unknown class labels of the data set will be known. With sub-cluster identification upon iterating the overall algorithm on the positive and negative clusters identified (until the clusters are no longer separable into sub-clusters), this method provides a way to cluster data sets without prior knowledge of the data's clustering characteristics, or the number of clusters."]},{"key":"dc:title","label":"Title","values":["External Support Vector Machine Clustering"]}]}],"canonical_facts":{"dc:contributor":["Winters-Hilt, Stephen","Fu, Bin","Tu, Shengru"],"dc:creator":["McChesney, Charlie"],"dc:description.abstract":["The external-Support Vector Machine (SVM) clustering algorithm clusters data vectors with no a priori knowledge of each vector's class. The algorithm works by first running a binary SVM against a data set, with each vector in the set randomly labeled, until the SVM converges. It then relabels data points that are mislabeled and a large distance from the SVM hyperplane. The SVM is then iteratively rerun followed by more label swapping until no more progress can be made. After this process, a high percentage of the previously unknown class labels of the data set will be known. With sub-cluster identification upon iterating the overall algorithm on the positive and negative clusters identified (until the clusters are no longer separable into sub-clusters), this method provides a way to cluster data sets without prior knowledge of the data's clustering characteristics, or the number of clusters."],"dc:identifier":["https://scholarworks.uno.edu/td/409"],"dc:title":["External Support Vector Machine Clustering"],"thesis:degree_discipline":["Computer Science"],"thesis:degree_level":["Thesis"],"thesis:degree_name":["M.S."]},"updated_at":"2026-07-24T05:28:22Z"}