{"id":{"repo_id":"baylor","oai_identifier":"oai:baylor-ir.tdl.org:2104/9248"},"canonical_url":"https://search.dev.ndltd.org/etd/baylor/oai:baylor-ir.tdl.org:2104/9248","repository":{"repo_id":"baylor","name":"Baylor University","base_url":"https://baylor-ir.tdl.org/server/oai/request"},"display":{"title":"A fast seeding technique for k-means algorithm.","abstract":"The k-means algorithm is one of the most popular clustering techniques because of its speed and simplicity. This algorithm is very simple and easy to understand and implement. The first step of this algorithm is choosing k initial cluster centers. The way that this set of initial cluster centers are chosen, have a great effect on speed and quality of k-means. One of the most popular seeding techniques is k-means++ initialization, but this method needs k passes over the dataset. The goal of this thesis is to propose a new seeding technique which chooses the initial centers much faster than k-means++.","abstract_html":"The k-means algorithm is one of the most popular clustering techniques because of its speed and simplicity. This algorithm is very simple and easy to understand and implement. The first step of this algorithm is choosing k initial cluster centers. The way that this set of initial cluster centers are chosen, have a great effect on speed and quality of k-means. One of the most popular seeding techniques is k-means++ initialization, but this method needs k passes over the dataset. The goal of this thesis is to propose a new seeding technique which chooses the initial centers much faster than k-means++.","abstract_has_math":false,"creators":["Karbasi, Seyedeh Paniz, 1986-"],"institution":"Baylor University.","degree_name":"M.S.","degree_level":"Masters","degree_discipline":null,"degree_department":null,"school":null,"contributors":[],"advisors":["Hamerly, Gregory James, 1977-"],"committee_chairs":[],"committee_members":[],"year":2014,"date_issued":"2014-12","date_published":"2014-12","updated_at":"2026-07-24T01:08:19Z","subjects":["K-means.","Seeding.","Clustering."],"languages":["en"],"rights":["Baylor University works are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. Contact libraryquestions@baylor.edu for inquiries about permission."],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/2104/9248","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Hamerly, Gregory James, 1977-"]},{"key":"dc:creator","label":"Author","values":["Karbasi, Seyedeh Paniz, 1986-"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2015-03-18T16:30:48Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2015-03-18T16:30:48Z"]},{"key":"dc:date.issued","label":"Date","values":["2014-12"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Masters"]},{"key":"thesis:degree_name","label":"Degree Name","values":["M.S."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["Baylor University."]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["K-means.","Seeding.","Clustering."]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language.iso","label":"Language (ISO)","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Baylor University works are protected by copyright. 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The goal of this thesis is to propose a new seeding technique which chooses the initial centers much faster than k-means++."]},{"key":"dc:format.mimetype","label":"Dc Format Mimetype","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["A fast seeding technique for k-means algorithm."]}]}],"canonical_facts":{"dc:contributor.advisor":["Hamerly, Gregory James, 1977-"],"dc:creator":["Karbasi, Seyedeh Paniz, 1986-"],"dc:date.accessioned":["2015-03-18T16:30:48Z"],"dc:date.available":["2015-03-18T16:30:48Z"],"dc:date.issued":["2014-12"],"dc:description.abstract":["The k-means algorithm is one of the most popular clustering techniques because of its speed and simplicity. This algorithm is very simple and easy to understand and implement. The first step of this algorithm is choosing k initial cluster centers. The way that this set of initial cluster centers are chosen, have a great effect on speed and quality of k-means. One of the most popular seeding techniques is k-means++ initialization, but this method needs k passes over the dataset. The goal of this thesis is to propose a new seeding technique which chooses the initial centers much faster than k-means++."],"dc:format.mimetype":["application/pdf"],"dc:identifier.uri":["https://hdl.handle.net/2104/9248"],"dc:language.iso":["en"],"dc:rights":["Baylor University works are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. Contact libraryquestions@baylor.edu for inquiries about permission."],"dc:subject":["K-means.","Seeding.","Clustering."],"dc:title":["A fast seeding technique for k-means algorithm."],"dc:type":["Thesis"],"thesis:degree_level":["Masters"],"thesis:degree_name":["M.S."],"thesis:institution_name":["Baylor University."]},"updated_at":"2026-07-24T01:08:19Z"}