A CLUE for CLUster Ensembles. Cluster ensembles are collections of individual solutions to a given clustering problem which are useful or necessary to consider in a wide range of applications. The R package clue provides an extensible computational environment for creating and analyzing cluster ensembles, with basic data structures for representing partitions and hierarchies, and facilities for computing on these, including methods for measuring proximity and obtaining consensus and ”secondary” clusterings.

References in zbMATH (referenced in 19 articles , 1 standard article )

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  1. Anderlucci, Laura; Fortunato, Francesca; Montanari, Angela: High-dimensional clustering via random projections (2022)
  2. Scutari, Marco; Denis, Jean-Baptiste: Bayesian networks. With examples in R (2022)
  3. Cristina Tortora, Ryan P. Browne, Aisha ElSherbiny, Brian C. Franczak, Paul D. McNicholas: Model-Based Clustering, Classification, and Discriminant Analysis Using the Generalized Hyperbolic Distribution: MixGHD R package (2021) not zbMATH
  4. Fernández-Durán, Juan José; Gregorio-Domínguez, María Mercedes: Consumer segmentation based on use patterns (2021)
  5. Jammalamadaka, S. Rao; Wainwright, Brian; Jin, Qianyu: Functional clustering on a circle using von Mises mixtures (2021)
  6. Casa, Alessandro; Chacón, José E.; Menardi, Giovanna: Modal clustering asymptotics with applications to bandwidth selection (2020)
  7. Kurt Hornik; Bettina Grün: movMF: An R Package for Fitting Mixtures of von Mises-Fisher Distributions (2014) not zbMATH
  8. 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
  9. Kurt Hornik; Ingo Feinerer; Martin Kober; Christian Buchta: Spherical k-Means Clustering (2012) not zbMATH
  10. Shen, Jeremy J.; Zhang, Nancy R.: Change-point model on nonhomogeneous Poisson processes with application in copy number profiling by next-generation DNA sequencing (2012)
  11. Bettina Grün; Kurt Hornik: topicmodels: An R Package for Fitting Topic Models (2011) not zbMATH
  12. Wu, Han-Ming: On biological validity indices for soft clustering algorithms for gene expression data (2011)
  13. Ayad, Hanan G.; Kamel, Mohamed S.: On voting-based consensus of cluster ensembles (2010)
  14. Fang Chang; Weiliang Qiu; Ruben Zamar; Ross Lazarus; Xiaogang Wang: clues: An R Package for Nonparametric Clustering Based on Local Shrinking (2010) not zbMATH
  15. Natthakan Iam-on; Simon Garrett: LinkCluE: A MATLAB Package for Link-Based Cluster Ensembles (2010) not zbMATH
  16. Jan de Leeuw; Kurt Hornik; Patrick Mair: Isotone Optimization in R: Pool-Adjacent-Violators Algorithm (PAVA) and Active Set Methods (2009) not zbMATH
  17. Murtagh, Fionn: The remarkable simplicity of very high dimensional data: application of model-based clustering (2009)
  18. Ingo Feinerer; Kurt Hornik; David Meyer: Text Mining Infrastructure in R (2008) not zbMATH
  19. Kurt Hornik: A CLUE for CLUster Ensembles (2005) not zbMATH