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 443 articles , 1 standard article )

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  1. Shkaberina, Guzel Sh.; Orlov, Viktor I.; Tovbis, Elena M.; Kazakovtsev, Lev A.: On the optimization models for automatic grouping of industrial products by homogeneous production batches (2020)
  2. Sies, Aniek; Van Mechelen, Iven: C443: a methodology to see a forest for the trees (2020)
  3. Surana, Amit: Koopman operator framework for time series modeling and analysis (2020)
  4. Tonellato, Stefano F.: Bayesian nonparametric clustering as a community detection problem (2020)
  5. Wang, Shanshan; Gartzke, Sebastian; Schreckenberg, Michael; Guhr, Thomas: Quasi-stationary states in temporal correlations for traffic systems: Cologne orbital motorway as an example (2020)
  6. Xie, Jiang; Xiong, Zhong-Yang; Dai, Qi-Zhu; Wang, Xiao-Xia; Zhang, Yu-Fang: A new internal index based on density core for clustering validation (2020)
  7. 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)
  8. 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)
  9. Brusco, Michael J.; Steinley, Douglas; Stevens, Jordan; Cradit, J. Dennis: Affinity propagation: an exemplar-based tool for clustering in psychological research (2019)
  10. Cossette, Hélène; Gadoury, Simon-Pierre; Marceau, Etienne; Robert, Christian Y.: Composite likelihood estimation method for hierarchical Archimedean copulas defined with multivariate compound distributions (2019)
  11. 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)
  12. Costilla, Roy; Liu, Ivy; Arnold, Richard; Fernández, Daniel: Bayesian model-based clustering for longitudinal ordinal data (2019)
  13. Dhaenens, Clarisse; Jourdan, Laetitia: Metaheuristics for data mining (2019)
  14. Diquigiovanni, Jacopo; Scarpa, Bruno: Analysis of association football playing styles: an innovative method to cluster networks (2019)
  15. D’Urso, Pierpaolo; Massari, Riccardo: Fuzzy clustering of mixed data (2019)
  16. Ferraro, Maria Brigida; Giordani, Paolo: A review and proposal of (fuzzy) clustering for nonlinearly separable data (2019)
  17. Galeano, Pedro; Peña, Daniel: Data science, big data and statistics (2019)
  18. Gao, Guangyuan; Wüthrich, Mario V.; Yang, Hanfang: Evaluation of driving risk at different speeds (2019)
  19. Gu, Jiaying; Volgushev, Stanislav: Panel data quantile regression with grouped fixed effects (2019)
  20. Hennig, Christian; Viroli, Cinzia; Anderlucci, Laura: Quantile-based clustering (2019)

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