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 438 articles , 1 standard article )

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  1. Joe, Kirbi; Gooyabadi, Maryam: A Bayesian nonparametric mixture model for studying universal patterns in color naming (2021)
  2. 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)
  3. Akhanli, Serhat Emre; Hennig, Christian: Comparing clusterings and numbers of clusters by aggregation of calibrated clustering validity indexes (2020)
  4. Anderson, Gordon; Pittau, Maria Grazia; Zelli, Roberto: Measuring the progress of equality of educational opportunity in absence of cardinal comparability (2020)
  5. Cabero, Ismael; Epifanio, Irene: Finding archetypal patterns for binary questionnaires (2020)
  6. Casa, Alessandro; Chacón, José E.; Menardi, Giovanna: Modal clustering asymptotics with applications to bandwidth selection (2020)
  7. de Amorim, Renato Cordeiro; Makarenkov, Vladimir; Mirkin, Boris: Core clustering as a tool for tackling noise in cluster labels (2020)
  8. Gan, Guojun; Valdez, Emiliano A.: Data clustering with actuarial applications (2020)
  9. Greco, Luca; Lucadamo, Antonio; Amenta, Pietro: An impartial trimming approach for joint dimension and sample reduction (2020)
  10. Heckens, Anton J.; Krause, Sebastian M.; Guhr, Thomas: Uncovering the dynamics of correlation structures relative to the collective market motion (2020)
  11. Hofmeyr, David P.: Degrees of freedom and model selection for (k)-means clustering (2020)
  12. Horejšová, Markéta; Vitali, Sebastiano; Kopa, Miloš; Moriggia, Vittorio: Evaluation of scenario reduction algorithms with nested distance (2020)
  13. Jia, Ziqi; Song, Ling: Weighted k-prototypes clustering algorithm based on the hybrid dissimilarity coefficient (2020)
  14. Kazakovtsev, Lev; Rozhnov, Ivan; Shkaberina, Guzel; Orlov, Viktor: (k)-means genetic algorithms with greedy genetic operators (2020)
  15. O’Brien, Jonathon J.; Lawson, Michael T.; Schweppe, Devin K.; Qaqish, Bahjat F.: Suboptimal comparison of partitions (2020)
  16. Shan, Qianqian; Hong, Yili; Meeker, William Q.: Seasonal warranty prediction based on recurrent event data (2020)
  17. 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)
  18. Sies, Aniek; Van Mechelen, Iven: C443: a methodology to see a forest for the trees (2020)
  19. Wang, Shanshan; Gartzke, Sebastian; Schreckenberg, Michael; Guhr, Thomas: Quasi-stationary states in temporal correlations for traffic systems: Cologne orbital motorway as an example (2020)
  20. 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)

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