Finding Groups In Data

By S. Seiler. 2016

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Classifying multi-model wheat yield impact response surfaces showing sensitivity to temperature and precipitation change (2017, unknown): “…for each crop were grouped by hierarchical clustering that minimizes the distances between members of each cluster using the agglomerative nesting algorithm (Kaufman and Rousseeuw, 1990) implemented in the R package “ cluster ” (Maechler et al., 2016) with the average method to determine clusters.”
Recovering the number of clusters in data sets with noise features using feature rescaling factors (2015, methodology): “The re-scaling approach could also benefit other clustering methods such as Partitioning around Medoids [23].”
Typology and distribution of small farms in Europe: Towards a better picture (2018, methodology): “clustering method, cluster analysis was implemented using the cluster package for R (Maechler et al., 2017), and the number of clusters was determined through the Gap Statistic (Tibshirani et al.”
Beyond subjective and objective in statistics (2015, methodology): “The subsequent cluster analysis (done by “partitioning around medoids”; Kaufman and Rousseeuw, 1990) would therefore separate the zero savings group strictly; no person with zero savings would appear together in a cluster with a person with nonzero savings.”