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Accuracy and robustness of clustering algorithms for small-size applications in bioinformatics
journal contribution
posted on 2023-06-08, 18:25 authored by Pamela Minicozzi, Fabio Rapallo, Enrico Scalas, Francesco DonderoThe 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.
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Publication status
- Published
Journal
Physica A Statistical Mechanics and its ApplicationsISSN
0378-4371Publisher
ElsevierExternal DOI
Issue
25Volume
387Page range
6310-6318Department affiliated with
- Mathematics Publications
Full text available
- No
Peer reviewed?
- Yes
Legacy Posted Date
2014-09-26Usage metrics
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