Resampling-based selective clustering ensembles
Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review
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Detail(s)
Original language | English |
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Pages (from-to) | 298-305 |
Journal / Publication | Pattern Recognition Letters |
Volume | 30 |
Issue number | 3 |
Publication status | Published - 1 Feb 2009 |
Link(s)
Abstract
Traditional clustering ensembles methods combine all obtained clustering results at hand. However, we observe that it can often achieve a better clustering solution if only part of all available clustering results are combined. This paper proposes a novel clustering ensembles method, termed as resampling-based selective clustering ensembles method. The proposed selective clustering ensembles method works by evaluating the qualities of all obtained clustering results through resampling technique and selectively choosing part of promising clustering results to build the ensemble committee. The final solution is obtained through combining the clustering results of the ensemble committee. Experimental results on several real data sets demonstrate that resampling-based selective clustering ensembles method is often able to achieve a better solution when compared with traditional clustering ensembles methods. © 2008 Elsevier B.V. All rights reserved.
Research Area(s)
- Clustering analysis, Clustering ensembles, Resampling technique
Citation Format(s)
Resampling-based selective clustering ensembles. / Hong, Yi; Kwong, Sam; Wang, Hanli et al.
In: Pattern Recognition Letters, Vol. 30, No. 3, 01.02.2009, p. 298-305.
In: Pattern Recognition Letters, Vol. 30, No. 3, 01.02.2009, p. 298-305.
Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review