References in zbMATH (referenced in 23 articles , 1 standard article )

Showing results 1 to 20 of 23.
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  1. Galimberti, Giuliano; Manisi, Annamaria; Soffritti, Gabriele: Modelling the role of variables in model-based cluster analysis (2018)
  2. Banerjee, Trambak; Mukherjee, Gourab; Radchenko, Peter: Feature screening in large scale cluster analysis (2017)
  3. Floriello, Davide; Vitelli, Valeria: Sparse clustering of functional data (2017)
  4. Kampert, Maarten M.; Meulman, Jacqueline J.; Friedman, Jerome H.: rCOSA: a software package for clustering objects on subsets of attributes (2017)
  5. Marbac, Matthieu; Sedki, Mohammed: Variable selection for model-based clustering using the integrated complete-data likelihood (2017)
  6. Ranalli, Monia; Rocci, Roberto: A model-based approach to simultaneous clustering and dimensional reduction of ordinal data (2017)
  7. Fraiman, Ricardo; Gimenez, Yanina; Svarc, Marcela: Seeking relevant information from a statistical model (2016)
  8. Fraiman, Ricardo; Gimenez, Yanina; Svarc, Marcela: Feature selection for functional data (2016)
  9. Wang, Yanhong; Fang, Yixin; Wang, Junhui: Sparse optimal discriminant clustering (2016)
  10. Plumb, Gregory; Pachauri, Deepti; Kondor, Risi; Singh, Vikas: $\Bbb S_n$FFT: a Julia toolkit for Fourier analysis of functions over permutations (2015)
  11. Powers, Scott; Hastie, Trevor; Tibshirani, Robert: Customized training with an application to mass spectrometric imaging of cancer tissue (2015)
  12. Tan, Kean Ming; Witten, Daniela: Statistical properties of convex clustering (2015)
  13. Zhang, Bohai; Konomi, Bledar A.; Sang, Huiyan; Karagiannis, Georgios; Lin, Guang: Full scale multi-output Gaussian process emulator with nonseparable auto-covariance functions (2015)
  14. Andrews, Jeffrey L.; McNicholas, Paul D.: Variable selection for clustering and classification (2014)
  15. Bouveyron, Charles; Brunet-Saumard, Camille: Discriminative variable selection for clustering with the sparse Fisher-EM algorithm (2014)
  16. Clémençon, Stéphan: A statistical view of clustering performance through the theory of $U$-processes (2014)
  17. Marchetti, Yuliya; Zhou, Qing: Solution path clustering with adaptive concave penalty (2014)
  18. McWilliams, Brian; Montana, Giovanni: Subspace clustering of high-dimensional data: a predictive approach (2014)
  19. 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) ioport
  20. Fang, Yixin; Wang, Junhui: Selection of the number of clusters via the bootstrap method (2012)

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