Validating clustering for gene expression data bioinformatics

Posted by / 04-Dec-2017 14:00

In 2003, Dembele and Kastner [13] described a modified fuzzy c-means algorithm applied to genomic data, which automatically selects the fuzziness parameter.

Finally, the use of nonnegative matrix factorization (NMF) was introduced in 2004 by Brunet .

Finally, we comment on the application of clustering to genomics in section 6.Although the ability of clustering algorithms to make inferences has been addressed to some extent, a mathematical foundation for clustering has been provided only very recently [19, 20].In this paper we will cover a mathematical model of clustering and review learning in section 2.Although used for many years in the context of gene expression microarray data, clustering has remained highly problematic [2, 12, 17].Some criticisms raise the question as to whether clustering can be used for scientific knowledge [18]: how may one judge the relative worth of clustering algorithms unless the assessment is based on their inference capabilities?

validating clustering for gene expression data bioinformatics-32validating clustering for gene expression data bioinformatics-65validating clustering for gene expression data bioinformatics-83

receives a set of vectors, and groups them based on a cost criterion or some other optimization rule.