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Proceedings Paper

K-means selective cluster ensembles based on multiple feature subsets
Author(s): Li Zhang; Weida Zhou; Haishuang Zou; Jieting Huo; Caili Wu; Licheng Jiao
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Paper 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.

Paper Details

Date Published: 30 October 2009
PDF: 6 pages
Proc. SPIE 7496, MIPPR 2009: Pattern Recognition and Computer Vision, 749627 (30 October 2009); doi: 10.1117/12.832625
Show Author Affiliations
Li Zhang, Xidian Univ. (China)
Weida Zhou, Xidian Univ. (China)
Haishuang Zou, Xidian Univ. (China)
Jieting Huo, Xidian Univ. (China)
Caili Wu, 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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