References in zbMATH (referenced in 56 articles , 1 standard article )

Showing results 1 to 20 of 56.
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  1. Feng, Zhangmei; Zhang, Jiamin: Nonparametric K-means algorithm with applications in economic and functional data (2022)
  2. Seo, Beomseok; Lin, Lin; Li, Jia: Block-wise variable selection for clustering via latent states of mixture models (2022)
  3. Pi, J.; Wang, Honggang; Pardalos, Panos M.: A dual reformulation and solution framework for regularized convex clustering problems (2021)
  4. Vera, J. Fernando; Macías, Rodrigo: On the behaviour of (K)-means clustering of a dissimilarity matrix by means of full multidimensional scaling (2021)
  5. Vouros, Avgoustinos; Langdell, Stephen; Croucher, Mike; Vasilaki, Eleni: An empirical comparison between stochastic and deterministic centroid initialisation for K-means variations (2021)
  6. Chakraborty, Saptarshi; Paul, Debolina; Das, Swagatam: Hierarchical clustering with optimal transport (2020)
  7. Duan, Leo L.: Latent simplex position model: high dimensional multi-view clustering with uncertainty quantification (2020)
  8. Kim, Youngseok; Gao, Chao: Bayesian model selection with graph structured sparsity (2020)
  9. Marbac, Matthieu; Sedki, Mohammed; Patin, Tienne: Variable selection for mixed data clustering: application in human population genomics (2020)
  10. Sanna Passino, Francesco; Heard, Nicholas A.: Bayesian estimation of the latent dimension and communities in stochastic blockmodels (2020)
  11. Wang, Wenjing; Zhang, Xin; Mai, Qing: Model-based clustering with envelopes (2020)
  12. Yang, Miin-Shen; Ali, Wajid: Fuzzy Gaussian lasso clustering with application to cancer data (2020)
  13. Brodinová, Šárka; Filzmoser, Peter; Ortner, Thomas; Breiteneder, Christian; Rohm, Maia: Robust and sparse (k)-means clustering for high-dimensional data (2019)
  14. Choi, Hosik; Lee, Seokho: Convex clustering for binary data (2019)
  15. 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)
  16. Galeano, Pedro; Peña, Daniel: Data science, big data and statistics (2019)
  17. Guillon, Arthur; Lesot, Marie-Jeanne; Marsala, Christophe: A proximal framework for fuzzy subspace clustering (2019)
  18. Lim, Yaeji; Oh, Hee-Seok; Cheung, Ying Kuen: Multiscale clustering for functional data (2019)
  19. Luo, Xiangyu; Wei, Yingying: Batch effects correction with unknown subtypes (2019)
  20. Marbac, Matthieu; Vandewalle, Vincent: A tractable multi-partitions clustering (2019)

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