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 23 articles , 1 standard article )

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  1. Akhanli, Serhat Emre; Hennig, Christian: Comparing clusterings and numbers of clusters by aggregation of calibrated clustering validity indexes (2020)
  2. Scitovski, Rudolf; Sabo, Kristian: Application of the \textttDIRECTalgorithm to searching for an optimal (k)-partition of the set (\mathcalA\subset\mathbbR^n) and its application to the multiple circle detection problem (2019)
  3. Alexander Foss; Marianthi Markatou: kamila: Clustering Mixed-Type Data in R and Hadoop (2018) not zbMATH
  4. Lee, Xing Ju; Hainy, Markus; McKeone, James P.; Drovandi, Christopher C.; Pettitt, Anthony N.: ABC model selection for spatial extremes models applied to south Australian maximum temperature data (2018)
  5. Dotto, Francesco; Farcomeni, Alessio; García-Escudero, Luis Angel; Mayo-Iscar, Agustín: A fuzzy approach to robust regression clustering (2017)
  6. Gagolewski, Marek: Penalty-based aggregation of multidimensional data (2017)
  7. Scitovski, Rudolf: A new global optimization method for a symmetric Lipschitz continuous function and the application to searching for a globally optimal partition of a one-dimensional set (2017)
  8. Marošević, Tomislav; Scitovski, Rudolf: Multiple ellipse fitting by center-based clustering (2015)
  9. Krey, Sebastian; Ligges, Uwe; Leisch, Friedrich: Music and timbre segmentation by recursive constrained (K)-means clustering (2014)
  10. Marošević, Tomislav: Data clustering for circle detection (2014)
  11. Olszewski, Dominik; Šter, Branko: Asymmetric clustering using the alpha-beta divergence (2014) ioport
  12. Sabo, Kristian; Scitovski, Rudolf: Interpretation and optimization of the (k)-means algorithm. (2014)
  13. Grbić, Ratko; Nyarko, Emmanuel Karlo; Scitovski, Rudolf: A modification of the \textttDIRECTmethod for Lipschitz global optimization for a symmetric function (2013)
  14. Sabo, Kristian; Scitovski, Rudolf; Vazler, Ivan: One-dimensional center-based l 1-clustering method (2013)
  15. Everitt, Brian; Hothorn, Torsten: An introduction to applied multivariate analysis with R. (2011)
  16. 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)
  17. Hua, Lin; Li, Dong-guo; Lin, Hui; Li, Lin; Li, Xia; Liu, Zhi-cheng: The correlation of gene expression and co-regulated gene patterns in characteristic KEGG pathways (2010)
  18. Grün, Bettina; Leisch, Friedrich: Dealing with label switching in mixture models under genuine multimodality (2009)
  19. Jiang, Tianyi; Tuzhilin, Alexander: Dynamic micro-targeting: fitness-based approach to predicting individual preferences (2009) ioport
  20. Boztuğ, Yasemin; Reutterer, Thomas: A combined approach for segment-specific market basket analysis (2008)

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