Fourth International Conference on Information, Communications and Signal Processing, 2003 and the Fourth Pacific Rim Conference on Multimedia. Proceedings of the 2003 Joint, 2003
Cluster analysis of gene expression data is useful for iden-biologically relevant groups of genes... more Cluster analysis of gene expression data is useful for iden-biologically relevant groups of genes. However, finding the correct clusters in the data and estimating the correct number of clusters are still two largely unsolved problems. In this paper, we propose a new clustering framework tbat is able to address both these problems. By using the oneprototype-take-onecluster (OFTOC) competitive learning paradigm, the proposed algorithm can 6nd natural clusters in the input data, and the clustering solntion is not sensitive to initialization. In order to estimate the number of distinct clusters in the data, an over-clustering and merging strategy is pmposed. For validation, we applied the new algorithm to both simulated gene expression data and real gene expression data (expression changes during yeast cell cycle). The results clearly mdicate the effectiveness of our method.
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Papers by Alan Liew