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

Pamela Minicozzi, Fabio Rapallo, Enrico Scalas and Francesco Dondero

Physica A: Statistical Mechanics and its Applications, 2008, vol. 387, issue 25, 6310-6318

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: Clustering; DNA microarray; Accuracy; Robustness (search for similar items in EconPapers)
Date: 2008
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:387:y:2008:i:25:p:6310-6318

DOI: 10.1016/j.physa.2008.07.026

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