References in zbMATH (referenced in 186 articles , 1 standard article )

Showing results 1 to 20 of 186.
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  1. Akalin, Altuna: Computational genomics with R. With the assistance of Verdan Franke, Bora Uyar and Jonathan Ronen (2021)
  2. Bagherinia, Ali; Minaei-Bidgoli, Behrooz; Hosseinzadeh, Mehdi; Parvin, Hamid: Reliability-based fuzzy clustering ensemble (2021)
  3. Batool, Fatima; Hennig, Christian: Clustering with the average silhouette width (2021)
  4. Fernández-Durán, Juan José; Gregorio-Domínguez, María Mercedes: Consumer segmentation based on use patterns (2021)
  5. Joe, Kirbi; Gooyabadi, Maryam: A Bayesian nonparametric mixture model for studying universal patterns in color naming (2021)
  6. Leung, Raymond; Balamurali, Mehala; Melkumyan, Arman: Sample truncation strategies for outlier removal in geochemical data: the MCD robust distance approach versus t-SNE ensemble clustering (2021)
  7. Sabater, Christian; Le Maître, Olivier; Congedo, Pietro Marco; Görtz, Stefan: A Bayesian approach for quantile optimization problems with high-dimensional uncertainty sources (2021)
  8. Wang, Y. X. Rachel; Li, Lexin; Li, Jingyi Jessica; Huang, Haiyan: Network modeling in biology: statistical methods for gene and brain networks (2021)
  9. Zöller, Marc-André; Huber, Marco F.: Benchmark and survey of automated machine learning frameworks (2021)
  10. Afridi, Mohammad Khan; Azam, Nouman; Yao, JingTao: Variance based three-way clustering approaches for handling overlapping clustering (2020)
  11. Alonso, Andrés M.; Galeano, Pedro; Peña, Daniel: A robust procedure to build dynamic factor models with cluster structure (2020)
  12. Dangl, Rainer; Leisch, Friedrich: Effects of resampling in determining the number of clusters in a data set (2020)
  13. Fordellone, Mario; Vichi, Maurizio: Finding groups in structural equation modeling through the partial least squares algorithm (2020)
  14. Gauchon, Romain; Loisel, Stéphane; Rullière, Jean-Louis: Health policyholder clustering using medical consumption. A useful tool for targeting prevention plans (2020)
  15. Guillaume, Serge; Ros, Frédéric: A family of unsupervised sampling algorithms (2020)
  16. Haslbeck, Jonas M. B.; Wulff, Dirk U.: Estimating the number of clusters via a corrected clustering instability (2020)
  17. Kazakovtsev, Lev; Rozhnov, Ivan; Shkaberina, Guzel; Orlov, Viktor: (k)-means genetic algorithms with greedy genetic operators (2020)
  18. Kazakovtsev, Lev; Shkaberina, Guzel; Rozhnov, Ivan; Li, Rui; Kazakovtsev, Vladimir: Genetic algorithms with the crossover-like mutation operator for the (k)-means problem (2020)
  19. Kim, Joonpyo; Oh, Hee-Seok: Pseudo-quantile functional data clustering (2020)
  20. Monsuru Adepeju, Samuel Langton, Jon Bannister : Akmedoids R package for generating directionally-homogeneous clusters of longitudinal data sets (2020) not zbMATH

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