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 447 articles , 1 standard article )

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  1. Batool, Fatima; Hennig, Christian: Clustering with the average silhouette width (2021)
  2. D’Ambrosio, Antonio; Amodio, Sonia; Iorio, Carmela; Pandolfo, Giuseppe; Siciliano, Roberta: Adjusted concordance index: an extensionl of the adjusted rand index to fuzzy partitions (2021)
  3. Joe, Kirbi; Gooyabadi, Maryam: A Bayesian nonparametric mixture model for studying universal patterns in color naming (2021)
  4. Saunders, K. R.; Stephenson, A. G.; Karoly, D. J.: A regionalisation approach for rainfall based on extremal dependence (2021)
  5. Tsai, Cary Chi-Liang; Cheng, Echo Sihan: Incorporating statistical clustering methods into mortality models to improve forecasting performances (2021)
  6. 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)
  7. Vinue, Guillermo; Epifanio, Irene: Robust archetypoids for anomaly detection in big functional data (2021)
  8. Akhanli, Serhat Emre; Hennig, Christian: Comparing clusterings and numbers of clusters by aggregation of calibrated clustering validity indexes (2020)
  9. Anderson, Gordon; Pittau, Maria Grazia; Zelli, Roberto: Measuring the progress of equality of educational opportunity in absence of cardinal comparability (2020)
  10. Bedru, Hayat Dino; Yu, Shuo; Xiao, Xinru; Zhang, Da; Wan, Liangtian; Guo, He; Xia, Feng: Big networks: a survey (2020)
  11. Cabero, Ismael; Epifanio, Irene: Finding archetypal patterns for binary questionnaires (2020)
  12. Casa, Alessandro; Chacón, José E.; Menardi, Giovanna: Modal clustering asymptotics with applications to bandwidth selection (2020)
  13. de Amorim, Renato Cordeiro; Makarenkov, Vladimir; Mirkin, Boris: Core clustering as a tool for tackling noise in cluster labels (2020)
  14. Gan, Guojun; Valdez, Emiliano A.: Data clustering with actuarial applications (2020)
  15. Greco, Luca; Lucadamo, Antonio; Amenta, Pietro: An impartial trimming approach for joint dimension and sample reduction (2020)
  16. Heckens, Anton J.; Krause, Sebastian M.; Guhr, Thomas: Uncovering the dynamics of correlation structures relative to the collective market motion (2020)
  17. Hofmeyr, David P.: Degrees of freedom and model selection for (k)-means clustering (2020)
  18. Horejšová, Markéta; Vitali, Sebastiano; Kopa, Miloš; Moriggia, Vittorio: Evaluation of scenario reduction algorithms with nested distance (2020)
  19. Jia, Ziqi; Song, Ling: Weighted k-prototypes clustering algorithm based on the hybrid dissimilarity coefficient (2020)
  20. Kazakovtsev, Lev; Rozhnov, Ivan; Shkaberina, Guzel; Orlov, Viktor: (k)-means genetic algorithms with greedy genetic operators (2020)

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