K-means selective cluster ensembles based on multiple feature subsets

Li Zhang; Weida Zhou; Haishuang Zou; Jieting Huo; Caili Wu; Licheng Jiao

doi:10.1117/12.832625

30 October 2009 K-means selective cluster ensembles based on multiple feature subsets

Li Zhang, Weida Zhou, Haishuang Zou, Jieting Huo, Caili Wu, Licheng Jiao

Proceedings Volume 7496, MIPPR 2009: Pattern Recognition and Computer Vision; 749627 (2009) https://doi.org/10.1117/12.832625
Event: Sixth International Symposium on Multispectral Image Processing and Pattern Recognition, 2009, Yichang, China

Abstract

Combining multiple clusterers is emerged as a powerful method for improving both the robustness and the stability of unsupervised classification solutions. In this paper, k-means selective cluster ensembles based on multiple feature subsets are proposed. In the ensemble, a random subset of features is used to train an individual k-means clusterer. In the final step, the selective weighted voting scheme is used for finding the best partition. The consensus function is constructed by relabeling all partitions of clusterers and finding the best partition. Experimental results on 4 UCI data sets show that our ensemble method can improve the clustering performance.

Citation Download Citation

Li Zhang, Weida Zhou, Haishuang Zou, Jieting Huo, Caili Wu, and Licheng Jiao "K-means selective cluster ensembles based on multiple feature subsets", Proc. SPIE 7496, MIPPR 2009: Pattern Recognition and Computer Vision, 749627 (30 October 2009); https://doi.org/10.1117/12.832625

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