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Incremental semi-supervised clustering ensemble for high dimensional data clustering

Authors
Yu, ZhiwenLuo, PeinanWu, SiHan, GuoqiangYou, JaneLeung, HaretonWong, Hau-SanZhang, Jun
Issue Date
Jun-2016
Publisher
Institute of Electrical and Electronics Engineers Inc.
Citation
2016 IEEE 32nd International Conference on Data Engineering (ICDE), pp 1484 - 1485
Pages
2
Indexed
SCI
SCOPUS
Journal Title
2016 IEEE 32nd International Conference on Data Engineering (ICDE)
Start Page
1484
End Page
1485
URI
https://scholarworks.bwise.kr/erica/handle/2021.sw.erica/116318
DOI
10.1109/ICDE.2016.7498386
ISSN
1084-4627
Abstract
Recently, cluster ensemble approaches have gained more and more attention [1]-[2], due to useful applications in the areas of pattern recognition, data mining, bioinformatics, and so on. When compared with traditional single clustering algorithms, cluster ensemble approaches are able to integrate multiple clustering solutions obtained from different data sources into a unified solution, and provide a more robust, stable and accurate final result. © 2016 IEEE.
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ERICA 공학대학 (SCHOOL OF ELECTRICAL ENGINEERING)
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