Cargando…
Robust clustering in high dimensional data using statistical depths
BACKGROUND: Mean-based clustering algorithms such as bisecting k-means generally lack robustness. Although componentwise median is a more robust alternative, it can be a poor center representative for high dimensional data. We need a new algorithm that is robust and works well in high dimensional da...
Autores principales: | Ding, Yuanyuan, Dang, Xin, Peng, Hanxiang, Wilkins, Dawn |
---|---|
Formato: | Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2007
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2099500/ https://www.ncbi.nlm.nih.gov/pubmed/18047731 http://dx.doi.org/10.1186/1471-2105-8-S7-S8 |
Ejemplares similares
-
Improving the Performance of SVM-RFE to Select Genes in Microarray Data
por: Ding, Yuanyuan, et al.
Publicado: (2006) -
Graph ranking for exploratory gene data analysis
por: Gao, Cuilan, et al.
Publicado: (2009) -
Statistics for approximate gene clusters
por: Jahn, Katharina, et al.
Publicado: (2013) -
Robust trend tests for genetic association in case-control studies using family data
por: Tian, Xin, et al.
Publicado: (2005) -
How to inherit statistically validated annotation within BAR+ protein clusters
por: Piovesan, Damiano, et al.
Publicado: (2013)