Two phase semi-supervised clustering using background knowledge
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Shin, Kwangcheol | - |
dc.contributor.author | Abraham, Ajith | - |
dc.date.accessioned | 2023-03-09T00:34:52Z | - |
dc.date.available | 2023-03-09T00:34:52Z | - |
dc.date.issued | 2006 | - |
dc.identifier.issn | 0302-9743 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/65455 | - |
dc.description.abstract | Using background knowledge in clustering, called semi-clustering, is one of the actively researched areas in data mining. In this paper, we illustrate how to use background knowledge related to a domain more efficiently. For a given data, the number of classes is investigated by using the must-link constraints before clustering and these must-link data are assigned to the corresponding classes. When the clustering algorithm is applied, we make use of the cannot-link constraints for assignment. The proposed clustering approach improves the result of COP k-means by about 10%. | - |
dc.format.extent | 6 | - |
dc.language | 영어 | - |
dc.language.iso | ENG | - |
dc.publisher | SPRINGER-VERLAG BERLIN | - |
dc.title | Two phase semi-supervised clustering using background knowledge | - |
dc.type | Article | - |
dc.identifier.doi | 10.1007/11875581_85 | - |
dc.identifier.bibliographicCitation | INTELLIGENT DATA ENGINEERING AND AUTOMATED LEARNING - IDEAL 2006, PROCEEDINGS, v.4224, pp 707 - 712 | - |
dc.description.isOpenAccess | N | - |
dc.identifier.wosid | 000241790900085 | - |
dc.identifier.scopusid | 2-s2.0-33750536006 | - |
dc.citation.endPage | 712 | - |
dc.citation.startPage | 707 | - |
dc.citation.title | INTELLIGENT DATA ENGINEERING AND AUTOMATED LEARNING - IDEAL 2006, PROCEEDINGS | - |
dc.citation.volume | 4224 | - |
dc.type.docType | Article; Proceedings Paper | - |
dc.publisher.location | 독일 | - |
dc.relation.journalResearchArea | Computer Science | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Artificial Intelligence | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Theory & Methods | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.
84, Heukseok-ro, Dongjak-gu, Seoul, Republic of Korea (06974)02-820-6194
COPYRIGHT 2019 Chung-Ang University All Rights Reserved.
Certain data included herein are derived from the © Web of Science of Clarivate Analytics. All rights reserved.
You may not copy or re-distribute this material in whole or in part without the prior written consent of Clarivate Analytics.