@article{c72553d6d5334ec4ba9ce6975c004753,
title = "Accuracy and robustness of clustering algorithms for small-size applications in bioinformatics",
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.",
keywords = "Accuracy, Clustering, DNA microarray, Robustness",
author = "Pamela Minicozzi and Fabio Rapallo and Enrico Scalas and Francesco Dondero",
note = "Funding Information: This paper is part of a research project devoted to understanding the effect of noise in unsupervised clustering methods and was also motivated by measurements carried out using microarrays on different environmental relevant model species exposed to adverse conditions, see Refs. [18–20] . This work has been supported by two Italian grants: MIUR PRIN 2006 (Minicozzi, Scalas, Dondero) and UPO “Ricerca Locale” 2007 (Rapallo).",
year = "2008",
month = nov,
day = "1",
doi = "10.1016/j.physa.2008.07.026",
language = "English",
volume = "387",
pages = "6310--6318",
journal = "Physica A: Statistical Mechanics and its Applications",
issn = "0378-4371",
publisher = "Elsevier",
number = "25",
}