clusterGeneration

R package clusterGeneration: Random Cluster Generation (with Specified Degree of Separation) .We developed the clusterGeneration package to provide functions for generating random clusters, generating random covariance/correlation matrices, calculating a separation index (data and population version) for pairs of clusters or cluster distributions, and 1-D and 2-D projection plots to visualize clusters. The package also contains a function to generate random clusters based on factorial designs with factors such as degree of separation, number of clusters, number of variables, number of noisy variables.


References in zbMATH (referenced in 17 articles )

Showing results 1 to 17 of 17.
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  1. Epskamp, Sacha; Isvoranu, Adela-Maria; Cheung, Mike W.-L.: Meta-analytic Gaussian network aggregation (2022)
  2. Matthew Trupiano: The R Package knnwtsim: Nonparametric Forecasting With a Tailored Similarity Measure (2021) arXiv
  3. Tomarchio, Salvatore D.; McNicholas, Paul D.; Punzo, Antonio: Matrix normal cluster-weighted models (2021)
  4. Dangl, Rainer; Leisch, Friedrich: Effects of resampling in determining the number of clusters in a data set (2020)
  5. Lai, Yuanhao; McLeod, Ian: Ensemble quantile classifier (2020)
  6. Flynt, Abby; Dean, Nema; Nugent, Rebecca: sARI: a soft agreement measure for class partitions incorporating assignment probabilities (2019)
  7. Šulc, Zdeněk; Řezanková, Hana: Comparison of similarity measures for categorical data in hierarchical clustering (2019)
  8. Andrews, Jeffrey L.: Addressing overfitting and underfitting in Gaussian model-based clustering (2018)
  9. Jeffrey Andrews; Jaymeson Wickins; Nicholas Boers; Paul McNicholas: teigen: An R Package for Model-Based Clustering and Classification via the Multivariate t Distribution (2018) not zbMATH
  10. Jin Zhu, Wenliang Pan, Wei Zheng, Xueqin Wang: Ball: An R package for detecting distribution difference and association in metric spaces (2018) arXiv
  11. Michael Hahsler and Matthew Bolaños and John Forrest: Introduction to stream: An Extensible Framework for Data Stream Clustering Research with R (2017) not zbMATH
  12. Audigier, Vincent; Husson, François; Josse, Julie: Multiple imputation for continuous variables using a Bayesian principal component analysis (2016)
  13. Vinué, Guillermo; Simó, Amelia; Alemany, Sandra: The (k)-means algorithm for 3D shapes with an application to apparel design (2016)
  14. Yang, Hu; Yi, Danhui; Yu, Chenqun: Cluster data streams with noisy variables (2016)
  15. Bruzzese, Dario; Vistocco, Domenico: DESPOTA: dendrogram slicing through a pemutation test approach (2015)
  16. Wei, Yuhong; McNicholas, Paul D.: Mixture model averaging for clustering (2015)
  17. Maiti, Saran Ishika; Maiti, Sadhan Samar: Maximum likelihood estimation of the linearly structured correlation matrix by a Jacobi-type iterative scheme (2013)