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 427 articles , 1 standard article )

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  1. van Delft, Anne; Dette, Holger: A similarity measure for second order properties of non-stationary functional time series with applications to clustering and testing (2021)
  2. Akhanli, Serhat Emre; Hennig, Christian: Comparing clusterings and numbers of clusters by aggregation of calibrated clustering validity indexes (2020)
  3. Anderson, Gordon; Pittau, Maria Grazia; Zelli, Roberto: Measuring the progress of equality of educational opportunity in absence of cardinal comparability (2020)
  4. Cabero, Ismael; Epifanio, Irene: Finding archetypal patterns for binary questionnaires (2020)
  5. Casa, Alessandro; Chacón, José E.; Menardi, Giovanna: Modal clustering asymptotics with applications to bandwidth selection (2020)
  6. de Amorim, Renato Cordeiro; Makarenkov, Vladimir; Mirkin, Boris: Core clustering as a tool for tackling noise in cluster labels (2020)
  7. Gan, Guojun; Valdez, Emiliano A.: Data clustering with actuarial applications (2020)
  8. Hofmeyr, David P.: Degrees of freedom and model selection for (k)-means clustering (2020)
  9. O’Brien, Jonathon J.; Lawson, Michael T.; Schweppe, Devin K.; Qaqish, Bahjat F.: Suboptimal comparison of partitions (2020)
  10. Shan, Qianqian; Hong, Yili; Meeker, William Q.: Seasonal warranty prediction based on recurrent event data (2020)
  11. 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)
  12. 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)
  13. Brusco, Michael J.; Steinley, Douglas; Stevens, Jordan; Cradit, J. Dennis: Affinity propagation: an exemplar-based tool for clustering in psychological research (2019)
  14. Ciaramella, Angelo; Staiano, Antonino: On the role of clustering and visualization techniques in gene microarray data (2019)
  15. Cossette, Hélène; Gadoury, Simon-Pierre; Marceau, Etienne; Robert, Christian Y.: Composite likelihood estimation method for hierarchical Archimedean copulas defined with multivariate compound distributions (2019)
  16. 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)
  17. Costilla, Roy; Liu, Ivy; Arnold, Richard; Fernández, Daniel: Bayesian model-based clustering for longitudinal ordinal data (2019)
  18. Dhaenens, Clarisse; Jourdan, Laetitia: Metaheuristics for data mining (2019)
  19. Diquigiovanni, Jacopo; Scarpa, Bruno: Analysis of association football playing styles: an innovative method to cluster networks (2019)
  20. Ferraro, Maria Brigida; Giordani, Paolo: A review and proposal of (fuzzy) clustering for nonlinearly separable data (2019)

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