clusfind

clusfind: A set of six stand-alone Fortran programs for cluster analysis. The programs are described and illustrated in the book ”Finding Groups in Data” by L. Kaufman and P.J. Rousseeuw, New York: John Wiley. Chapter 1: DAISY.FOR (computes dissimilarities); Chapter 2: PAM.FOR (partitions the data set into clusters with a new method using medoids); Chapter 3: CLARA.FOR (for clustering large applications); Chapter 4: FANNY.FOR (a new method for fuzzy clustering); Chapter 5+6 : TWINS.FOR (hierarchical clustering; you can choose between agglomerative and divisive); Chapter 7: MONA.FOR (divisive hierachical clustering of binary data sets.


References in zbMATH (referenced in 400 articles , 1 standard article )

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  1. Casa, Alessandro; Chacón, José E.; Menardi, Giovanna: Modal clustering asymptotics with applications to bandwidth selection (2020)
  2. Yoder, Jordan; Chen, Li; Pao, Henry; Bridgeford, Eric; Levin, Keith; Fishkind, Donniell E.; Priebe, Carey; Lyzinski, Vince: Vertex nomination: the canonical sampling and the extended spectral nomination schemes (2020)
  3. Boiarov, A. A.; Granichin, O. N.: Stochastic approximation algorithm with randomization at the input for unsupervised parameters estimation of Gaussian mixture model with sparse parameters (2019)
  4. Brusco, Michael J.; Steinley, Douglas; Stevens, Jordan; Cradit, J. Dennis: Affinity propagation: an exemplar-based tool for clustering in psychological research (2019)
  5. Cossette, Hélène; Gadoury, Simon-Pierre; Marceau, Etienne; Robert, Christian Y.: Composite likelihood estimation method for hierarchical Archimedean copulas defined with multivariate compound distributions (2019)
  6. Costa, Marcelo Azevedo; Mineti, Leandro Brioschi; Mayrink, Vinícius Diniz; Lopes, Ana Lúcia Miranda: Bayesian detection of clusters in efficiency score maps: an application to Brazilian energy regulation (2019)
  7. Costilla, Roy; Liu, Ivy; Arnold, Richard; Fernández, Daniel: Bayesian model-based clustering for longitudinal ordinal data (2019)
  8. Dhaenens, Clarisse; Jourdan, Laetitia: Metaheuristics for data mining (2019)
  9. Ferraro, Maria Brigida; Giordani, Paolo: A review and proposal of (fuzzy) clustering for nonlinearly separable data (2019)
  10. Galeano, Pedro; Peña, Daniel: Data science, big data and statistics (2019)
  11. Gao, Guangyuan; Wüthrich, Mario V.; Yang, Hanfang: Evaluation of driving risk at different speeds (2019)
  12. Gu, Jiaying; Volgushev, Stanislav: Panel data quantile regression with grouped fixed effects (2019)
  13. Hennig, Christian; Viroli, Cinzia; Anderlucci, Laura: Quantile-based clustering (2019)
  14. Jensen, Melanie A.; Wang, Ying-Ying; Lai, Samuel K.; Forest, M. Gregory; McKinley, Scott A.: Antibody-mediated immobilization of virions in mucus (2019)
  15. Martino, Andrea; Ghiglietti, Andrea; Ieva, Francesca; Paganoni, Anna Maria: A (k)-means procedure based on a Mahalanobis type distance for clustering multivariate functional data (2019)
  16. Melnykov, Volodymyr; Zhu, Xuwen: An extension of the (K)-means algorithm to clustering skewed data (2019)
  17. Niu, Feng gao: Basic co-occurrence latent semantic vector space model (2019)
  18. Pham-Toan, D.; Vo-Van, T.; Pham-Chau, A. T.; Nguyen-Trang, T.; Ho-Kieu, D.: A new binary adaptive elitist differential evolution based automatic (k)-medoids clustering for probability density functions (2019)
  19. Sukhorukova, N.: Two curve Chebyshev approximation and its application to signal clustering (2019)
  20. Yuan, Beibei; Heiser, Willem; De Rooij, Mark: The (\delta)-machine: classification based on distances towards prototypes (2019)

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