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.
Accuracy and robustness of clustering algorithms for small-size applications in bioinformatics / Minicozzi, P; Rapallo, Fabio; Scalas, Enrico; Dondero, Francesco. - In: PHYSICA. A. - ISSN 0378-4371. - 387:25(2008), pp. 6310-6318. [10.1016/j.physa.2008.07.026]
Accuracy and robustness of clustering algorithms for small-size applications in bioinformatics
SCALAS, Enrico;
2008
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.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
