Clustering with multiple distance metrics - mixture models with profile transformations

Abstract

Clustering methods often require the selection of a distance metric; how do we define data objects as ’close’ enough to be grouped together, or ’far’ enough apart to be separated? Choosing an appropriate distance metric is not always easy. We consider high-dimensional gene expression data as an example. The shape of a gene’s expression profile across… (More)

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Cite this paper

@inproceedings{Jrnsten2007ClusteringWM, title={Clustering with multiple distance metrics - mixture models with profile transformations}, author={Rebecka J{\"o}rnsten}, year={2007} }