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 27 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. Dangl, Rainer; Leisch, Friedrich: Effects of resampling in determining the number of clusters in a data set (2020)
  3. Li, Hui: Multiple ellipse fitting of densely connected contours (2019)
  4. 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)
  5. Alexander Foss; Marianthi Markatou: kamila: Clustering Mixed-Type Data in R and Hadoop (2018) not zbMATH
  6. 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)
  7. Scitovski, Rudolf; Sušac, Marijana Zekić; Has, Adela: Searching for an optimal partition of incomplete data with application in modeling energy efficiency of public buildings (2018)
  8. Dotto, Francesco; Farcomeni, Alessio; García-Escudero, Luis Angel; Mayo-Iscar, Agustín: A fuzzy approach to robust regression clustering (2017)
  9. Gagolewski, Marek: Penalty-based aggregation of multidimensional data (2017)
  10. 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)
  11. Cena, Anna; Gagolewski, Marek: Fuzzy k-minpen clustering and k-nearest-minpen classification procedures incorporating generic distance-based penalty minimizers (2016)
  12. Marošević, Tomislav; Scitovski, Rudolf: Multiple ellipse fitting by center-based clustering (2015)
  13. Krey, Sebastian; Ligges, Uwe; Leisch, Friedrich: Music and timbre segmentation by recursive constrained (K)-means clustering (2014)
  14. Marošević, Tomislav: Data clustering for circle detection (2014)
  15. Olszewski, Dominik; Šter, Branko: Asymmetric clustering using the alpha-beta divergence (2014) ioport
  16. Sabo, Kristian; Scitovski, Rudolf: Interpretation and optimization of the (k)-means algorithm. (2014)
  17. Grbić, Ratko; Nyarko, Emmanuel Karlo; Scitovski, Rudolf: A modification of the \textttDIRECTmethod for Lipschitz global optimization for a symmetric function (2013)
  18. Sabo, Kristian; Scitovski, Rudolf; Vazler, Ivan: One-dimensional center-based l 1-clustering method (2013)
  19. Everitt, Brian; Hothorn, Torsten: An introduction to applied multivariate analysis with R. (2011)
  20. 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)

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