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.

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  1. Jing, Bingyi; Li, Ting; Ying, Ningchen; Yu, Xianshi: Community detection in sparse networks using the symmetrized Laplacian inverse matrix (SLIM) (2022)
  2. Modak, Soumita: A new nonparametric interpoint distance-based measure for assessment of clustering (2022)
  3. Scaldelai, D.; Matioli, L. C.; Santos, S. R.; Kleina, M.: MulticlusterKDE: a new algorithm for clustering based on multivariate kernel density estimation (2022)
  4. Tverskoi, Denis; Gavrilets, Sergey: The evolution of germ-soma specialization under different genetic and environmental effects (2022)
  5. Batool, Fatima; Hennig, Christian: Clustering with the average silhouette width (2021)
  6. 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)
  7. Joe, Kirbi; Gooyabadi, Maryam: A Bayesian nonparametric mixture model for studying universal patterns in color naming (2021)
  8. Jung, Sungkyu; Park, Kiho; Kim, Byungwon: Clustering on the torus by conformal prediction (2021)
  9. López-Rodríguez, Domingo; Cordero, Pablo; Enciso, Manuel; Mora, Ángel: Clustering and identification of core implications (2021)
  10. Młodak, Andrzej: (k)-means, Ward and probabilistic distance-based clustering methods with contiguity constraint (2021)
  11. Namdari, Alireza; Durrani, Tariq S.: A multilayer feedforward perceptron model in neural networks for predicting stock market short-term trends (2021)
  12. Pelle, Elvira; Pappadà, Roberta: A clustering procedure for mixed-type data to explore ego network typologies: an application to elderly people living alone in Italy (2021)
  13. Pięta, Piotr; Szmuc, Tomasz: Applications of rough sets in big data analysis: an overview (2021)
  14. Punzo, Antonio; Tortora, Cristina: Multiple scaled contaminated normal distribution and its application in clustering (2021)
  15. Rohrbeck, Christian; Tawn, Jonathan A.: Bayesian spatial clustering of extremal behavior for hydrological variables (2021)
  16. Saunders, K. R.; Stephenson, A. G.; Karoly, D. J.: A regionalisation approach for rainfall based on extremal dependence (2021)
  17. Thrun, Michael C.; Ultsch, Alfred: Using projection-based clustering to find distance- and density-based clusters in high-dimensional data (2021)
  18. Thrun, Michael C.; Ultsch, Alfred: Swarm intelligence for self-organized clustering (2021)
  19. Torrente, Aurora; Romo, Juan: Initializing (k)-means clustering by bootstrap and data depth (2021)
  20. Tsai, Cary Chi-Liang; Cheng, Echo Sihan: Incorporating statistical clustering methods into mortality models to improve forecasting performances (2021)

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