Accuracy and robustness of clustering algorithms for small-size applications in bioinformatics

Minicozzi, Pamela, Rapallo, Fabio, Scalas, Enrico and Dondero, Francesco (2008) Accuracy and robustness of clustering algorithms for small-size applications in bioinformatics. Physica A Statistical Mechanics and its Applications, 387 (25). pp. 6310-6318. ISSN 0378-4371

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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.

Item Type: Article
Schools and Departments: School of Mathematical and Physical Sciences > Mathematics
Subjects: Q Science > QA Mathematics > QA0276 Mathematical statistics
Depositing User: Enrico Scalas
Date Deposited: 26 Sep 2014 08:09
Last Modified: 26 Sep 2014 08:09
URI: http://sro.sussex.ac.uk/id/eprint/50260
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