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. Li, Tao: A unified view on clustering binary data (2006) ioport
  2. Maier, H. R.; Zecchin, A. C.; Radbone, L.; Goonan, P.: Optimising the mutual information of ecological data clusters using evolutionary algorithms (2006)
  3. Peters, Georg: Some refinements of rough (k)-means clustering (2006)
  4. Pianykh, Oleg S.: Analytically tractable case of fuzzy c-means clustering (2006)
  5. Qiu, Weiliang; Joe, Harry: Separation index and partial membership for clustering (2006)
  6. Reynolds, A. P.; Richards, G.; de la Iglesia, B.; Rayward-Smith, V. J.: Clustering rules: A comparison of partitioning and hierarchical clustering algorithms (2006)
  7. Sheng, Weiguo; Liu, Xiaohui: A genetic (k)-medoids clustering algorithm (2006) ioport
  8. Smyth, Christine; Coomans, Danny; Everingham, Yvette: Clustering noisy data in a reduced dimension space via multivariate regression trees (2006)
  9. Valentini, Giorgio; Ruffino, Francesca: Characterization of lung tumor subtypes through gene expression cluster validity assessment (2006)
  10. Vijaya, P. A.; Murty, M. Narasimha; Subramanian, D. K.: Efficient bottom-up hybrid hierarchical clustering techniques for protein sequence classification (2006)
  11. Vijaya, P. A.; Murty, M. Narasimha; Subramanian, D. K.: Efficient median based clustering and classification techniques for protein sequences (2006) ioport
  12. Agrawal, Rakesh; Gehrke, Johannes; Gunopulos, Dimitrios; Raghavan, Prabhakar: Automatic subspace clustering of high dimensional data (2005) ioport
  13. Frigui, Hichem: Unsupervised learning of arbitrarily shaped clusters using ensembles of Gaussian models (2005)
  14. Hu, Tianming; Sung, Sam Yuan: Clustering spatial data with a hybrid EM approach (2005) ioport
  15. Kang, Sung Jin; Lee, Myoungjae: Q-convergence with interquartile ranges (2005)
  16. Liao, T. Warren: Clustering of time series data -- a survey (2005)
  17. Pernkopf, Franz: Bayesian network classifiers versus selective (k)-NN classifier (2005)
  18. Prabhu, Nagabhushana; Chang, Hung-Chieh; deguzman, Maria: Optimization on Lie manifolds and pattern recognition (2005)
  19. Qin, A. K.; Suganthan, P. N.: Enhanced neural gas network for prototype-based clustering (2005) ioport
  20. Shou, Yutao; Mamoulis, Nikos; Cheung, David W.: Fast and exact warping of time series using adaptive segmental approximations (2005)

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