References in zbMATH (referenced in 13 articles , 1 standard article )

Showing results 1 to 13 of 13.
Sorted by year (citations)

  1. Boehmke, Brad; Greenwell, Brandon M.: Hands-on machine learning with R (2020)
  2. Giordani, Paolo; Ferraro, Maria Brigida; Martella, Francesca: An introduction to clustering with R (2020)
  3. Isotta Landi, Veronica Mandelli, Michael V. Lombardo: reval: a Python package to determine the best number of clusters with stability-based relative clustering validation (2020) arXiv
  4. François Role, Stanislas Morbieu, Mohamed Nadif: CoClust: A Python Package for Co-Clustering (2019) not zbMATH
  5. He, Zhenfeng; Yu, Chunyan: Clustering stability-based evolutionary K-means (2019)
  6. Kharoubi, Rachid; Oualkacha, Karim; Mkhadri, Abdallah: The cluster correlation-network support vector machine for high-dimensional binary classification (2019)
  7. Mair, Patrick: Modern psychometrics with R (2018)
  8. Plaza, Francisco; Salas, Rodrigo; Yáñez, Eleuterio: Identifying ecosystem patterns from time series of anchovy (\textitEngraulisringens) and sardine (\textitSardinopssagax) landings in northern Chile (2018)
  9. Rahmanishamsi, Jafar; Dolati, Ali; Aghabozorgi, Masoudreza R.: A copula based ICA algorithm and its application to time series clustering (2018)
  10. Dehmer, Matthias (ed.); Shi, Yongtang (ed.); Emmert-Streib, Frank (ed.): Computational network analysis with R. Applications in biology, medicine and chemistry (2017)
  11. Goulet, D.: Modeling, simulating, and parameter Fitting of biochemical kinetic experiments (2016)
  12. Bruzzese, Dario; Vistocco, Domenico: DESPOTA: dendrogram slicing through a pemutation test approach (2015)
  13. Malika Charrad; Nadia Ghazzali; Véronique Boiteau; Azam Niknafs: NbClust: An R Package for Determining the Relevant Number of Clusters in a Data Set (2014) not zbMATH