SigClust
sigclust: Statistical Significance of Clustering. SigClust is a statistical method for testing the significance of clustering results. SigClust can be applied to assess the statistical significance of splitting a data set into two clusters. For more than two clusters, SigClust can be used iteratively.
Keywords for this software
References in zbMATH (referenced in 14 articles )
Showing results 1 to 14 of 14.
Sorted by year (- Wang, Miaoyan; Fischer, Jonathan; Song, Yun S.: Three-way clustering of multi-tissue multi-individual gene expression data using semi-nonnegative tensor decomposition (2019)
- Cybis, Gabriela B.; Valk, Marcio; Lopes, Sílvia R. C.: Clustering and classification problems in genetics through (U)-statistics (2018)
- Dong, Ping; Lin, Lu; Song, Yunquan: Significance test of clustering under high dimensional setting with applications to cancer data (2018)
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- Huang, Hanwen; Liu, Yufeng; Hayes, David Neil; Nobel, Andrew; Marron, J. S.; Hennig, Christian: Significance testing in clustering (2016)
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- Lee, Myung Hee: On the border of extreme and mild spiked models in the HDLSS framework (2012)
- Krzanowski, Wojtek J.; Hand, David J.: A simple method for screening variables before clustering microarray data (2009)
- Liu, Yufeng; Hayes, David Neil; Nobel, Andrew; Marron, J. S.: Statistical significance of clustering for high-dimension, low-sample size data (2008)