30 October 2009 Center matching scheme for k-means cluster ensembles
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Proceedings Volume 7496, MIPPR 2009: Pattern Recognition and Computer Vision; 749614 (2009) https://doi.org/10.1117/12.832603
Event: Sixth International Symposium on Multispectral Image Processing and Pattern Recognition, 2009, Yichang, China
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.
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Li Zhang, Li Zhang, Weida Zhou, Weida Zhou, Caili Wu, Caili Wu, Jieting Huo, Jieting Huo, Haishuang Zou, Haishuang Zou, Licheng Jiao, Licheng Jiao, } "Center matching scheme for k-means cluster ensembles", Proc. SPIE 7496, MIPPR 2009: Pattern Recognition and Computer Vision, 749614 (30 October 2009); doi: 10.1117/12.832603; https://doi.org/10.1117/12.832603
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