Density of points clustering, application to transcriptomic data analysis.

@article{Wicker2002DensityOP,
  title={Density of points clustering, application to transcriptomic data analysis.},
  author={Nicolas Wicker and Doulaye Demb{\'e}l{\'e} and Wolfgang Raffelsberger and Olivier Poch},
  journal={Nucleic acids research},
  year={2002},
  volume={30 18},
  pages={3992-4000}
}
With the increasing amount of data produced by high-throughput technologies in many fields of science, clustering has become an integral step in exploratory data analysis in order to group similar elements into classes. However, many clustering algorithms can only work properly if aided by human expertise. For example, one parameter which is crucial and often manually set is the number of clusters present in the analyzed set. We present a novel stopping rule to find the optimal number of… CONTINUE READING
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Mclust: software for model-based cluster analysis

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