Incremental semi-supervised clustering ensemble for high dimensional data clustering
- Authors
- Yu, Zhiwen; Luo, Peinan; Wu, Si; Han, Guoqiang; You, Jane; Leung, Hareton; Wong, Hau-San; Zhang, 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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Collections - COLLEGE OF ENGINEERING SCIENCES > SCHOOL OF ELECTRICAL ENGINEERING > 1. Journal Articles
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