{"id":{"repo_id":"must-thes","oai_identifier":"oai:scholarsmine.mst.edu:doctoral_dissertations-2592"},"canonical_url":"https://search.dev.ndltd.org/etd/must-thes/oai:scholarsmine.mst.edu:doctoral_dissertations-2592","repository":{"repo_id":"must-thes","name":"Missouri University of Science and Technology","base_url":"https://scholarsmine.mst.edu/do/oai/"},"display":{"title":"A generalization based hybrid algorithm for clustering semi-structured data","abstract":"\"In this work, a generalized based methodology that combines attribute hierarchy construction, object generalization and data clustering is presented. The algorithm works well on semi-structured data and requires only a minimum of domain knowledge. Since the algorithm reduces the dimensionality of the semi-structured data, clustering of the resulting generalized data often requires less execution time and computer memory\"--Abstract, page iii.","abstract_html":""In this work, a generalized based methodology that combines attribute hierarchy construction, object generalization and data clustering is presented. The algorithm works well on semi-structured data and requires only a minimum of domain knowledge. Since the algorithm reduces the dimensionality of the semi-structured data, clustering of the resulting generalized data often requires less execution time and computer memory"--Abstract, page iii.","abstract_has_math":false,"creators":["Shih, Ming-Yi"],"institution":"University of Missouri--Rolla","degree_name":"Ph. D. in Computer Science","degree_level":null,"degree_discipline":null,"degree_department":null,"school":null,"contributors":[],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2016,"date_issued":"2016-02-10T08:00:00Z","date_published":"2016-02-10T08:00:00Z","updated_at":"2026-07-24T03:19:12Z","subjects":["Computer Sciences"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://scholarsmine.mst.edu/doctoral_dissertations/1590","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:creator","label":"Author","values":["Shih, Ming-Yi"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.available","label":"Dc Date Available","values":["2016-02-10T08:00:00Z"]},{"key":"dc:type","label":"Dc Type","values":["Dissertation - Citation"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Ph. D. in Computer Science"]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Missouri--Rolla"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Computer Sciences"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://scholarsmine.mst.edu/doctoral_dissertations/1590"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["\"In this work, a generalized based methodology that combines attribute hierarchy construction, object generalization and data clustering is presented. The algorithm works well on semi-structured data and requires only a minimum of domain knowledge. Since the algorithm reduces the dimensionality of the semi-structured data, clustering of the resulting generalized data often requires less execution time and computer memory\"--Abstract, page iii."]},{"key":"dc:title","label":"Title","values":["A generalization based hybrid algorithm for clustering semi-structured data"]}]}],"canonical_facts":{"dc:creator":["Shih, Ming-Yi"],"dc:date.available":["2016-02-10T08:00:00Z"],"dc:description.abstract":["\"In this work, a generalized based methodology that combines attribute hierarchy construction, object generalization and data clustering is presented. The algorithm works well on semi-structured data and requires only a minimum of domain knowledge. Since the algorithm reduces the dimensionality of the semi-structured data, clustering of the resulting generalized data often requires less execution time and computer memory\"--Abstract, page iii."],"dc:identifier":["https://scholarsmine.mst.edu/doctoral_dissertations/1590"],"dc:subject":["Computer Sciences"],"dc:title":["A generalization based hybrid algorithm for clustering semi-structured data"],"dc:type":["Dissertation - Citation"],"thesis:degree_name":["Ph. D. in Computer Science"],"thesis:institution_name":["University of Missouri--Rolla"]},"updated_at":"2026-07-24T03:19:12Z"}