In EmilHvitfeldt/celery: A Common API to Clustering

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r descr_models("k_means", "ClusterR")

Tuning Parameters

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defaults <- 
  tibble::tibble(tidyclust = c("num_clusters"),
                 default = c("no default"))

param <-
 k_means() |> 
  set_engine("ClusterR") |> 
  set_mode("partition") |> 
  make_parameter_list(defaults)

This model has r nrow(param) tuning parameters:

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param$item

Translation from tidyclust to the original package (partition)

#| label: ClusterR-cls
k_means(num_clusters = integer(1)) |> 
  set_engine("ClusterR") |> 
  set_mode("partition") |> 
  translate_tidyclust()

Preprocessing requirements

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References

Forgy, E. W. (1965). Cluster analysis of multivariate data: efficiency vs interpretability of classifications. Biometrics, 21, 768–769.
Hartigan, J. A. and Wong, M. A. (1979). Algorithm AS 136: A K-means clustering algorithm. Applied Statistics, 28, 100–108. doi:10.2307/2346830.
Lloyd, S. P. (1957, 1982). Least squares quantization in PCM. Technical Note, Bell Laboratories. Published in 1982 in IEEE Transactions on Information Theory, 28, 128–137.
MacQueen, J. (1967). Some methods for classification and analysis of multivariate observations. In Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability, eds L. M. Le Cam & J. Neyman, 1, pp. 281–297. Berkeley, CA: University of California Press.