Identifying Projected Clusters from Gene Expression Profiles
Kevin Yip2004, Journal of Biomedical Informatics
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Abstract
In microarray gene expression data, clusters may hide in subspaces. Traditional clustering algorithms that make use of similarity measurements in the full input space may fail to detect the clusters. In recent years a number of algo- rithms have been proposed to identify this kind of projected clusters, but many of them rely on some critical parame- ters whose proper
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