R package sparcl: Perform sparse hierarchical clustering and sparse k-means clustering. Implements the sparse clustering methods of Witten and Tibshirani (2010): ”A framework for feature selection in clustering”; published in Journal of the American Statistical Association 105(490): 713-726.

References in zbMATH (referenced in 48 articles , 1 standard article )

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  1. Chakraborty, Saptarshi; Paul, Debolina; Das, Swagatam: Hierarchical clustering with optimal transport (2020)
  2. Duan, Leo L.: Latent simplex position model: high dimensional multi-view clustering with uncertainty quantification (2020)
  3. Kim, Youngseok; Gao, Chao: Bayesian model selection with graph structured sparsity (2020)
  4. Marbac, Matthieu; Sedki, Mohammed; Patin, Tienne: Variable selection for mixed data clustering: application in human population genomics (2020)
  5. Sanna Passino, Francesco; Heard, Nicholas A.: Bayesian estimation of the latent dimension and communities in stochastic blockmodels (2020)
  6. Wang, Wenjing; Zhang, Xin; Mai, Qing: Model-based clustering with envelopes (2020)
  7. Brodinová, Šárka; Filzmoser, Peter; Ortner, Thomas; Breiteneder, Christian; Rohm, Maia: Robust and sparse (k)-means clustering for high-dimensional data (2019)
  8. Choi, Hosik; Lee, Seokho: Convex clustering for binary data (2019)
  9. Crook, Oliver M.; Gatto, Laurent; Kirk, Paul D. W.: Fast approximate inference for variable selection in Dirichlet process mixtures, with an application to pan-cancer proteomics (2019)
  10. Galeano, Pedro; Peña, Daniel: Data science, big data and statistics (2019)
  11. Guillon, Arthur; Lesot, Marie-Jeanne; Marsala, Christophe: A proximal framework for fuzzy subspace clustering (2019)
  12. Lim, Yaeji; Oh, Hee-Seok; Cheung, Ying Kuen: Multiscale clustering for functional data (2019)
  13. Luo, Xiangyu; Wei, Yingying: Batch effects correction with unknown subtypes (2019)
  14. Marbac, Matthieu; Vandewalle, Vincent: A tractable multi-partitions clustering (2019)
  15. Chekouo, Thierry; Murua, Alejandro: High-dimensional variable selection with the plaid mixture model for clustering (2018)
  16. Fop, Michael; Murphy, Thomas Brendan: Variable selection methods for model-based clustering (2018)
  17. Galimberti, Giuliano; Manisi, Annamaria; Soffritti, Gabriele: Modelling the role of variables in model-based cluster analysis (2018)
  18. Luca Scrucca; Adrian Raftery: clustvarsel: A Package Implementing Variable Selection for Gaussian Model-Based Clustering in R (2018) not zbMATH
  19. Wallace, Meredith L.; Buysse, Daniel J.; Germain, Anne; Hall, Martica H.; Iyengar, Satish: Variable selection for skewed model-based clustering: application to the identification of novel sleep phenotypes (2018)
  20. Arias-Castro, Ery; Pu, Xiao: A simple approach to sparse clustering (2017)

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