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 13 articles , 1 standard article )

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  1. Fraiman, Ricardo; Gimenez, Yanina; Svarc, Marcela: Feature selection for functional data (2016)
  2. Wang, Yanhong; Fang, Yixin; Wang, Junhui: Sparse optimal discriminant clustering (2016)
  3. Plumb, Gregory; Pachauri, Deepti; Kondor, Risi; Singh, Vikas: $\Bbb S_n$FFT: a Julia toolkit for Fourier analysis of functions over permutations (2015)
  4. Andrews, Jeffrey L.; McNicholas, Paul D.: Variable selection for clustering and classification (2014)
  5. Bouveyron, Charles; Brunet-Saumard, Camille: Discriminative variable selection for clustering with the sparse Fisher-EM algorithm (2014)
  6. Clémençon, Stéphan: A statistical view of clustering performance through the theory of $U$-processes (2014)
  7. Marchetti, Yuliya; Zhou, Qing: Solution path clustering with adaptive concave penalty (2014)
  8. McWilliams, Brian; Montana, Giovanni: Subspace clustering of high-dimensional data: a predictive approach (2014)
  9. Quintana-Pacheco, Yuri; Ruiz-Fernández, Daniel; Magrans-Rico, Agustín: Growing neural gas approach for obtaining homogeneous maps by restricting the insertion of new nodes (2014)
  10. Fang, Yixin; Wang, Junhui: Selection of the number of clusters via the bootstrap method (2012)
  11. Sun, Wei; Wang, Junhui; Fang, Yixin: Regularized $k$-means clustering of high-dimensional data and its asymptotic consistency (2012)
  12. Celeux, Gilles; Martin-Magniette, Marie-Laure; Maugis, Cathy; Raftery, Adrian E.: Comment on “A framework for feature selection in clustering” (2011)
  13. Witten, Daniela M.; Tibshirani, Robert: A framework for feature selection in clustering (2010)