Abstract
This paper introduces a wavelet transformation and a cluster ensemble framework using graph theory for clustering gene expression data sets. The experiment results indicate that wavelet transformation and cluster ensemble approaches together yield better clustering results than the single best clustering algorithm on both synthetic and yeast gene expression data sets.
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