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dc.contributor.author이상호*
dc.date.accessioned2018-07-18T16:30:58Z-
dc.date.available2018-07-18T16:30:58Z-
dc.date.issued2006*
dc.identifier.isbn3540464913*
dc.identifier.isbn9783540464914*
dc.identifier.issn0302-9743*
dc.identifier.otherOAK-17736*
dc.identifier.urihttps://dspace.ewha.ac.kr/handle/2015.oak/245060-
dc.description.abstractWe propose a novel method, called heterogeneous clustering ensemble (HCE), to generate robust clustering results that combine multiple partitions (clusters) derived from various clustering algorithms. The proposed method combines partitions of various clustering algorithms by means of newly-proposed the selection and the crossover operation of the genetic algorithm (GA) during the evolutionary process. © Springer-Verlag Berlin Heidelberg 2006.*
dc.languageEnglish*
dc.titleA novel framework for discovering robust cluster results*
dc.typeConference Paper*
dc.relation.volume4265 LNAI*
dc.relation.indexSCOPUS*
dc.relation.startpage373*
dc.relation.lastpage377*
dc.relation.journaltitleLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)*
dc.identifier.scopusid2-s2.0-33750709733*
dc.author.googleYoon H.-S.*
dc.author.googleLee S.-H.*
dc.author.googleCho S.-B.*
dc.author.googleKim J.H.*
dc.contributor.scopusid이상호(56812941400)*
dc.date.modifydate20240322133058*
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인공지능대학 > 컴퓨터공학과 > Journal papers
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