Discriminative Clustering of Yeast Stress Response

Kaski, Samuel; Nikkilä, Janne; Savia, Eerika; Roos, Christophe

doi:10.1007/10950913_4

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Cited by 4 publications

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“…which have successfully been applied to various problems in the biomedical domain such as gene expression analysis, inspection of mass spectrometric data, healthcare, analysis of microarray data, protein sequences, medical image analysis, etc. [1,37,36,41,44,53,54].…”

Section: Introductionmentioning

confidence: 99%

Median Topographic Maps for Biomedical Data Sets

Hammer

Hasenfuss

Rossi

2009

Lecture Notes in Computer Science

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Median clustering extends popular neural data analysis methods such as the self-organizing map or neural gas to general data structures given by a dissimilarity matrix only. This offers flexible and robust global data inspection methods which are particularly suited for a variety of data as occurs in biomedical domains. In this chapter, we give an overview about median clustering and its properties and extensions, with a particular focus on efficient implementations adapted to large scale data analysis.

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