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Proceedings Paper

Center matching scheme for k-means cluster ensembles
Author(s): Li Zhang; Weida Zhou; Caili Wu; Jieting Huo; Haishuang Zou; Licheng Jiao
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Paper Abstract

In this paper, a center matching scheme is proposed for constructing a consensus function in the k-means cluster ensemble learning. Each k-means clusterer outputs a sequence with k cluster centers. We randomly select a cluster center sequence as a reference one, and then we rearrange the other cluster center sequences according to the reference sequence. Then we label the data using these matched cluster center sequences. Hence we get multiple partitions or clusterings. Finally, multiple clusterings are combined to the best labeling by using combination rules, such as the majority voting rule, the weighted voting rule and the selective weighted voting rule. Experimental results on 7 UCI data sets show that our ensemble methods could improve the clustering results effectively.

Paper Details

Date Published: 30 October 2009
PDF: 6 pages
Proc. SPIE 7496, MIPPR 2009: Pattern Recognition and Computer Vision, 749614 (30 October 2009); doi: 10.1117/12.832603
Show Author Affiliations
Li Zhang, Xidian Univ. (China)
Weida Zhou, Xidian Univ. (China)
Caili Wu, Xidian Univ. (China)
Jieting Huo, Xidian Univ. (China)
Haishuang Zou, Xidian Univ. (China)
Licheng Jiao, Xidian Univ. (China)

Published in SPIE Proceedings Vol. 7496:
MIPPR 2009: Pattern Recognition and Computer Vision
Mingyue Ding; Bir Bhanu; Friedrich M. Wahl; Jonathan Roberts, Editor(s)

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