Incremental semi-supervised clustering ensemble for high dimensional data clustering

Zhiwen Yu; Peinan Luo; Si Wu; Guoqiang Han; Jia You; Hareton Leung; Hau San Wong; Jun Zhang

doi:10.1109/ICDE.2016.7498386

Incremental semi-supervised clustering ensemble for high dimensional data clustering

Zhiwen Yu, Peinan Luo, Si Wu, Guoqiang Han, Jia You, Hareton Leung, Hau San Wong, Jun Zhang

Research output: Chapter in book / Conference proceeding › Conference article published in proceeding or book › Academic research › peer-review

8 Citations (Scopus)

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.

Original language	English
Title of host publication	2016 IEEE 32nd International Conference on Data Engineering, ICDE 2016
Publisher	IEEE
Pages	1484-1485
Number of pages	2
ISBN (Electronic)	9781509020195
DOIs	https://doi.org/10.1109/ICDE.2016.7498386
Publication status	Published - 22 Jun 2016
Event	32nd IEEE International Conference on Data Engineering, ICDE 2016 - Helsinki, Finland Duration: 16 May 2016 → 20 May 2016

Conference

Conference	32nd IEEE International Conference on Data Engineering, ICDE 2016
Country/Territory	Finland
City	Helsinki
Period	16/05/16 → 20/05/16

ASJC Scopus subject areas

Artificial Intelligence
Computational Theory and Mathematics
Computer Graphics and Computer-Aided Design
Computer Networks and Communications
Information Systems
Information Systems and Management

More information

10.1109/ICDE.2016.7498386

Cite this

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title = "Incremental semi-supervised clustering ensemble for high dimensional data clustering",

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.",

author = "Zhiwen Yu and Peinan Luo and Si Wu and Guoqiang Han and Jia You and Hareton Leung and Wong, {Hau San} and Jun Zhang",

year = "2016",

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doi = "10.1109/ICDE.2016.7498386",

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booktitle = "2016 IEEE 32nd International Conference on Data Engineering, ICDE 2016",

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Yu, Z, Luo, P, Wu, S, Han, G, You, J, Leung, H, Wong, HS & Zhang, J 2016, Incremental semi-supervised clustering ensemble for high dimensional data clustering. in 2016 IEEE 32nd International Conference on Data Engineering, ICDE 2016., 7498386, IEEE, pp. 1484-1485, 32nd IEEE International Conference on Data Engineering, ICDE 2016, Helsinki, Finland, 16/05/16. https://doi.org/10.1109/ICDE.2016.7498386

Incremental semi-supervised clustering ensemble for high dimensional data clustering. / Yu, Zhiwen; Luo, Peinan; Wu, Si et al.
2016 IEEE 32nd International Conference on Data Engineering, ICDE 2016. IEEE, 2016. p. 1484-1485 7498386.

Research output: Chapter in book / Conference proceeding › Conference article published in proceeding or book › Academic research › peer-review

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AU - You, Jia

AU - Leung, Hareton

AU - Wong, Hau San

AU - Zhang, Jun

PY - 2016/6/22

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N2 - 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.

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Incremental semi-supervised clustering ensemble for high dimensional data clustering

Abstract

Conference

ASJC Scopus subject areas

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