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

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References in zbMATH (referenced in 14 articles )

Showing results 1 to 14 of 14.
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  1. Wang, Miaoyan; Fischer, Jonathan; Song, Yun S.: Three-way clustering of multi-tissue multi-individual gene expression data using semi-nonnegative tensor decomposition (2019)
  2. Cybis, Gabriela B.; Valk, Marcio; Lopes, Sílvia R. C.: Clustering and classification problems in genetics through (U)-statistics (2018)
  3. Dong, Ping; Lin, Lu; Song, Yunquan: Significance test of clustering under high dimensional setting with applications to cancer data (2018)
  4. Neville, Zachariah; Brownstein, Naomi C.: Macros to conduct tests of multimodality in SAS (2018)
  5. Chen, Guanhua; Liu, Yufeng; Shen, Dinggang; Kosorok, Michael R.: Composite large margin classifiers with latent subclasses for heterogeneous biomedical data (2016)
  6. Huang, Hanwen; Liu, Yufeng; Hayes, David Neil; Nobel, Andrew; Marron, J. S.; Hennig, Christian: Significance testing in clustering (2016)
  7. Lu, Qiyi; Qiao, Xingye: Significance analysis of high-dimensional, low-sample size partially labeled data (2016)
  8. Bruzzese, Dario; Vistocco, Domenico: DESPOTA: dendrogram slicing through a pemutation test approach (2015)
  9. Qiao, Xingye; Zhang, Lingsong: Flexible high-dimensional classification machines and their asymptotic properties (2015)
  10. Xiong, Jie; Dittmer, D. P.; Marron, J. S.: “Virus hunting” using radial distance weighted discrimination (2015)
  11. Lim, Johan; Lee, Sungim; Park, Heon-Jin; Lee, Kyeong Eun; Lee, Shin-Jae: Bootstrap method to evaluate tightness of clusters with application to the Korean standard occlusion study (2014)
  12. Lee, Myung Hee: On the border of extreme and mild spiked models in the HDLSS framework (2012)
  13. Krzanowski, Wojtek J.; Hand, David J.: A simple method for screening variables before clustering microarray data (2009)
  14. Liu, Yufeng; Hayes, David Neil; Nobel, Andrew; Marron, J. S.: Statistical significance of clustering for high-dimension, low-sample size data (2008)