flexclust

flexclust: Flexible Cluster Algorithms , The main function kcca implements a general framework for k-centroids cluster analysis supporting arbitrary distance measures and centroid computation. Further cluster methods include hard competitive learning, neural gas, and QT clustering. There are numerous visualization methods for cluster results (neighborhood graphs, convex cluster hulls, barcharts of centroids, ...), and bootstrap methods for the analysis of cluster stability. (Source: http://cran.r-project.org/web/packages)


References in zbMATH (referenced in 12 articles , 1 standard article )

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  1. Krey, Sebastian; Ligges, Uwe; Leisch, Friedrich: Music and timbre segmentation by recursive constrained $K$-means clustering (2014)
  2. Olszewski, Dominik; Šter, Branko: Asymmetric clustering using the alpha-beta divergence (2014)
  3. Sabo, Kristian; Scitovski, Rudolf: Interpretation and optimization of the $k$-means algorithm. (2014)
  4. Grbić, Ratko; Nyarko, Emmanuel Karlo; Scitovski, Rudolf: A modification of the DIRECT method for Lipschitz global optimization for a symmetric function (2013)
  5. Sabo, Kristian; Scitovski, Rudolf; Vazler, Ivan: One-dimensional center-based l 1-clustering method (2013)
  6. Everitt, Brian; Hothorn, Torsten: An introduction to applied multivariate analysis with R. (2011)
  7. Chiang, Mark Ming-Tso; Mirkin, Boris: Intelligent choice of the number of clusters in $K$-means clustering: an experimental study with different cluster spreads (2010)
  8. Grün, Bettina; Leisch, Friedrich: Dealing with label switching in mixture models under genuine multimodality (2009)
  9. Jiang, Tianyi; Tuzhilin, Alexander: Dynamic micro-targeting: fitness-based approach to predicting individual preferences (2009)
  10. Boztuğ, Yasemin; Reutterer, Thomas: A combined approach for segment-specific market basket analysis (2008)
  11. Leisch, Friedrich: Visualizing cluster analysis and finite mixture models (2008)
  12. Leisch, Friedrich: A toolbox for $K$-centroids cluster analysis (2006)