Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
977766 | Physica A: Statistical Mechanics and its Applications | 2008 | 9 Pages |
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 a posteriori criterion to choose between two discordant clustering algorithm is presented.
Related Topics
Physical Sciences and Engineering
Mathematics
Mathematical Physics
Authors
Pamela Minicozzi, Fabio Rapallo, Enrico Scalas, Francesco Dondero,