Accuracy and Robustness of Clustering Algorithms for Small-Size Applications in Bioinformatics
Applications
2009-11-13 v1
Abstract
The performance (accuracy and robustness) of several clustering algorithms is studied for linearly dependent random variables in the presence of noise. It turns out that the error percentage quickly increases when the number of observations is less than the number of variables. This situation is common situation in experiments with DNA microarrays. Moreover, an {\it a posteriori} criterion to choose between two discordant clustering algorithm is presented.
Cite
@article{arxiv.0807.3838,
title = {Accuracy and Robustness of Clustering Algorithms for Small-Size Applications in Bioinformatics},
author = {Pamela Minicozzi and Fabio Rapallo and Enrico Scalas and Francesco Dondero},
journal= {arXiv preprint arXiv:0807.3838},
year = {2009}
}
Comments
20 pages, 6 figures