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Genetic weighted k-means algorithm for clustering large-scale gene expression data
BACKGROUND: The traditional (unweighted) k-means is one of the most popular clustering methods for analyzing gene expression data. However, it suffers three major shortcomings. It is sensitive to initial partitions, its result is prone to the local minima, and it is only applicable to data with sphe...
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| Formato: | Texto |
| Lenguaje: | English |
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BioMed Central
2008
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| Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2423435/ https://www.ncbi.nlm.nih.gov/pubmed/18541047 http://dx.doi.org/10.1186/1471-2105-9-S6-S12 |