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 7 articles )

Showing results 1 to 7 of 7.
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  1. Lu, Qiyi; Qiao, Xingye: Significance analysis of high-dimensional, low-sample size partially labeled data (2016)
  2. Bruzzese, Dario; Vistocco, Domenico: DESPOTA: dendrogram slicing through a pemutation test approach (2015)
  3. Qiao, Xingye; Zhang, Lingsong: Flexible high-dimensional classification machines and their asymptotic properties (2015)
  4. Xiong, Jie; Dittmer, D.P.; Marron, J.S.: “Virus hunting” using radial distance weighted discrimination (2015)
  5. Lee, Myung Hee: On the border of extreme and mild spiked models in the HDLSS framework (2012)
  6. Krzanowski, Wojtek J.; Hand, David J.: A simple method for screening variables before clustering microarray data (2009)
  7. Liu, Yufeng; Hayes, David Neil; Nobel, Andrew; Marron, J.S.: Statistical significance of clustering for high-dimension, low-sample size data (2008)