fpc

R package fpc: Flexible procedures for clustering. Various methods for clustering and cluster validation. Fixed point clustering. Linear regression clustering. Clustering by merging Gaussian mixture components. Symmetric and asymmetric discriminant projections for visualisation of the separation of groupings. Cluster validation statistics for distance based clustering including corrected Rand index. Cluster-wise cluster stability assessment. Methods for estimation of the number of clusters: Calinski-Harabasz, Tibshirani and Walther’s prediction strength, Fang and Wang’s bootstrap stability. Gaussian/multinomial mixture fitting for mixed continuous/categorical variables. Variable-wise statistics for cluster interpretation. DBSCAN clustering. Interface functions for many clustering methods implemented in R, including estimating the number of clusters with kmeans, pam and clara. Modality diagnosis for Gaussian mixtures. For an overview see package?fpc.


References in zbMATH (referenced in 29 articles )

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  1. Hennig, Christian: An empirical comparison and characterisation of nine popular clustering methods (2022)
  2. Alonso, Andrés M.; D’Urso, Pierpaolo; Gamboa, Carolina; Guerrero, Vanesa: Cophenetic-based fuzzy clustering of time series by linear dependency (2021)
  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. Freitas, Adelaide; Macedo, Eloísa; Vichi, Maurizio: An empirical comparison of two approaches for CDPCA in high-dimensional data (2021)
  5. Fu, Wei; Perry, Patrick O.: Estimating the number of clusters using cross-validation (2020)
  6. Giordani, Paolo; Ferraro, Maria Brigida; Martella, Francesca: An introduction to clustering with R (2020)
  7. Gupta, Bhisham C.; Guttman, Irwin; Jayalath, Kalanka P.: Statistics and probability with applications for engineers and scientists using MINITAB, R and JMP (2020)
  8. Alfonso Iodice D’Enza, Angelos Markos, Michel van de Velden: Beyond Tandem Analysis: Joint Dimension Reduction and Clustering in R (2019) not zbMATH
  9. Chacón, José E.: Mixture model modal clustering (2019)
  10. Ferraro, Maria Brigida; Giordani, Paolo: A review and proposal of (fuzzy) clustering for nonlinearly separable data (2019)
  11. Michael Hahsler; Matthew Piekenbrock; Derek Doran: dbscan: Fast Density-Based Clustering with R (2019) not zbMATH
  12. Alexander Foss; Marianthi Markatou: kamila: Clustering Mixed-Type Data in R and Hadoop (2018) not zbMATH
  13. Angelo Mazza; Antonio Punzo; Salvatore Ingrassia: flexCWM: A Flexible Framework for Cluster-Weighted Models (2018) not zbMATH
  14. Wentz, J. M.; Mendenhall, A. R.; Bortz, D. M.: Pattern formation in the longevity-related expression of heat shock protein-16.2 in Caenorhabditis elegans (2018)
  15. Michael Hahsler and Matthew Bolaños and John Forrest: Introduction to stream: An Extensible Framework for Data Stream Clustering Research with R (2017) not zbMATH
  16. Foss, Alex; Markatou, Marianthi; Ray, Bonnie; Heching, Aliza: A semiparametric method for clustering mixed data (2016)
  17. Roy Chowdhury, Niladri; Cook, Dianne; Hofmann, Heike; Majumder, Mahbubul; Lee, Eun-Kyung; Toth, Amy L.: Using visual statistical inference to better understand random class separations in high dimension, low sample size data (2015)
  18. Pablo Montero; José Vilar: TSclust: An R Package for Time Series Clustering (2014) not zbMATH
  19. Adelchi Azzalini, Giovanna Menardi: Clustering Via Nonparametric Density Estimation: the R Package pdfCluster (2013) arXiv
  20. Brian P. Kent, Alessandro Rinaldo, Timothy Verstynen: DeBaCl: A Python Package for Interactive DEnsity-BAsed CLustering (2013) arXiv

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