A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2019; you can also visit the original URL.
The file type is
This work performs a data driven comparative study of clustering methods used in the analysis of gene expression time courses (or time series). Five clustering methods found in the literature of gene expression analysis are compared: agglomerative hierarchical clustering, CLICK, dynamical clustering, k-means and self-organizing maps. In order to evaluate the methods, a k-fold cross-validation procedure adapted to unsupervised methods is applied. The accuracy of the results is assessed by thedoi:10.1590/s1415-47572004000400025 fatcat:m666xdkzhbhyhcx3pmlvr3k65a