R package cclust: Convex Clustering Methods and Clustering Indexes. Convex Clustering methods, including K-means algorithm, On-line Update algorithm (Hard Competitive Learning) and Neural Gas algorithm (Soft Competitive Learning), and calculation of several indexes for finding the number of clusters in a data set.
Keywords for this software
References in zbMATH (referenced in 11 articles )
Showing results 1 to 11 of 11.
- Roy, Dooti; Deshpande, Ved; Linder, M. Henry: A cluster-based taxonomy of bus crashes in the united states (2021)
- Thrun, Michael C.; Ultsch, Alfred: Using projection-based clustering to find distance- and density-based clusters in high-dimensional data (2021)
- Dehmer, Matthias (ed.); Shi, Yongtang (ed.); Emmert-Streib, Frank (ed.): Computational network analysis with R. Applications in biology, medicine and chemistry (2017)
- Malika Charrad; Nadia Ghazzali; Véronique Boiteau; Azam Niknafs: NbClust: An R Package for Determining the Relevant Number of Clusters in a Data Set (2014) not zbMATH
- Sabo, Miroslav: Consensus clustering with differential evolution (2014)
- Vahid Nia; Anthony Davison: High-Dimensional Bayesian Clustering with Variable Selection: The R Package bclust (2012) not zbMATH
- Kraus, Johann M.; Müssel, Christoph; Palm, Günther; Kestler, Hans A.: Multi-objective selection for collecting cluster alternatives (2011)
- Fang Chang; Weiliang Qiu; Ruben Zamar; Ross Lazarus; Xiaogang Wang: clues: An R Package for Nonparametric Clustering Based on Local Shrinking (2010) not zbMATH
- Guy Brock; Vasyl Pihur; Susmita Datta; Somnath Datta: clValid: An R Package for Cluster Validation (2008) not zbMATH
- Leisch, Friedrich: A toolbox for (K)-centroids cluster analysis (2006)
- Kurt Hornik: A CLUE for CLUster Ensembles (2005) not zbMATH