A k-means clustering algorithm

By John Hartigan, M. A. Wong. 1979

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Citations in the corpus

blockCV: an R package for generating spatially or environmentally separated folds for k-fold cross-validation of species distribution models (2018, methodology): “This strategy uses clustering methods ( k - means; Hartigan & Wong, 1979) to specify sets of similar environmental conditions based on the input covariates and the chosen number of clusters in (possibly multivariate) environmental space.”
A CLUE for CLUster Ensembles (2005, unknown): “This may result in degenerate solutions, and will be replaced by a Hartigan-Wong (Hartigan and Wong 1979) style algorithm eventually.”
Comparing genomic selection and marker-assisted selection for Fusarium head blight resistance in wheat (Triticum aestivum L.) (2016, methodology): “Clusters were obtained in JMP Pro 12 (SAS Institute 2015b) using the k-means clustering algorithm (Hartigan and Wong 1979) on marker data (Ly et al. 2013; Arruda et al. 2015).”
Cross‐cultural differences in survey response patterns (2007, methodology): “Respondents’ answer patterns were partitioned using the k-means algorithm (Hartigan and Wong, 1979).”
Detecting Fertility and Early Embryo Development of Chicken Eggs Using Near-Infrared Hyperspectral Imaging (2013, methodology): “The K-means clustering (Hartigan and Wong 1979) algorithm is a well-known unsupervised classification method which generates a specific number of disjoint, flat (non-hierarchical) clusters by minimizing the sum, over all clusters, of the within-class distances from each object to its cluster center.”
Faster k-Medoids Clustering: Improving the PAM, CLARA, and CLARANS Algorithms (2018, background): “It optimizes the sum of squared Euclidean distances, and even then the textbook algorithm may end up slightly oﬀ a local optimum, because always assigning a point to its nearest center can increase the variance by moving the centers away from other points (Hartigan and Wong, 1979).”
Ckmeans.1d.dp: Optimal k-means Clustering in One Dimension by Dynamic Programming (2011, methodology): “We used the Hartigan and Wong (1979) implementation of k -means, as provided by the kmeans() function in R.”
Multiclass Real-Time Intent Recognition of a Powered Lower Limb Prosthesis (2009, unknown): “In this paper, the reduced dataset for an activity mode wi , was roughly clustered using the k-means algorithm [27].”
NbClust: An R Package for Determining the Relevant Number of Clusters in a Data Set (2014, methodology): “k -means is an iterative method which minimizes the within-class sum of squares for a given number of clusters (MacQueen 1967; Hartigan and Wong 1979).”