DC Field | Value | Language |
---|---|---|
dc.contributor.author | Lee, D | - |
dc.contributor.author | Lee, J | - |
dc.date.accessioned | 2016-04-01T02:51:41Z | - |
dc.date.available | 2016-04-01T02:51:41Z | - |
dc.date.created | 2010-06-14 | - |
dc.date.issued | 2010-06 | - |
dc.identifier.issn | 1041-4347 | - |
dc.identifier.other | 2010-OAK-0000021372 | - |
dc.identifier.uri | https://oasis.postech.ac.kr/handle/2014.oak/25887 | - |
dc.description.abstract | Clustering methods utilizing support estimates of a data distribution have recently attracted much attention because of their ability to generate cluster boundaries of arbitrary shape and to deal with outliers efficiently. In this paper, we propose a novel dissimilarity measure based on a dynamical system associated with support estimating functions. Theoretical foundations of the proposed measure are developed and applied to construct a clustering method that can effectively partition the whole data space. Simulation results demonstrate that clustering based on the proposed dissimilarity measure is robust to the choice of kernel parameters and able to control the number of clusters efficiently. | - |
dc.description.statementofresponsibility | X | - |
dc.language | English | - |
dc.publisher | IEEE COMPUTER SOC | - |
dc.relation.isPartOf | IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING | - |
dc.subject | Clustering | - |
dc.subject | kernel methods | - |
dc.subject | dynamical systems | - |
dc.subject | equilibrium vector | - |
dc.subject | support | - |
dc.subject | VECTOR | - |
dc.subject | CLASSIFICATION | - |
dc.subject | OPTIMIZATION | - |
dc.title | Dynamic Dissimilarity Measure for Support-Based Clustering | - |
dc.type | Article | - |
dc.contributor.college | 산업경영공학과 | - |
dc.identifier.doi | 10.1109/TKDE.2009.140 | - |
dc.author.google | Lee, D | - |
dc.author.google | Lee, J | - |
dc.relation.volume | 22 | - |
dc.relation.issue | 6 | - |
dc.relation.startpage | 900 | - |
dc.relation.lastpage | 905 | - |
dc.contributor.id | 10081901 | - |
dc.relation.journal | IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING | - |
dc.relation.index | SCI급, SCOPUS 등재논문 | - |
dc.relation.sci | SCI | - |
dc.collections.name | Journal Papers | - |
dc.type.rims | ART | - |
dc.identifier.bibliographicCitation | IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, v.22, no.6, pp.900 - 905 | - |
dc.identifier.wosid | 000276801300012 | - |
dc.date.tcdate | 2019-02-01 | - |
dc.citation.endPage | 905 | - |
dc.citation.number | 6 | - |
dc.citation.startPage | 900 | - |
dc.citation.title | IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING | - |
dc.citation.volume | 22 | - |
dc.contributor.affiliatedAuthor | Lee, J | - |
dc.identifier.scopusid | 2-s2.0-77951765930 | - |
dc.description.journalClass | 1 | - |
dc.description.journalClass | 1 | - |
dc.description.wostc | 26 | - |
dc.description.scptc | 31 | * |
dc.date.scptcdate | 2018-05-121 | * |
dc.type.docType | Article | - |
dc.subject.keywordAuthor | Clustering | - |
dc.subject.keywordAuthor | kernel methods | - |
dc.subject.keywordAuthor | dynamical systems | - |
dc.subject.keywordAuthor | equilibrium vector | - |
dc.subject.keywordAuthor | support | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Artificial Intelligence | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Information Systems | - |
dc.relation.journalWebOfScienceCategory | Engineering, Electrical & Electronic | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Computer Science | - |
dc.relation.journalResearchArea | Engineering | - |
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