In tidyclust: A Common API to Clustering

r descr_models("hier_clust", "stats")

Tuning Parameters

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

param <-
 hier_clust() %>% 
  set_engine("stats") %>% 
  set_mode("partition") %>% 
  make_parameter_list(defaults)

This model has r nrow(param) tuning parameters:

param$item

Translation from tidyclust to the original package (partition)

hier_clust(num_clusters = integer(1)) %>% 
  set_engine("stats") %>% 
  set_mode("partition") %>% 
  translate_tidyclust()

Preprocessing requirements

References

Becker, R. A., Chambers, J. M. and Wilks, A. R. (1988). The New S Language. Wadsworth & Brooks/Cole. (S version.)
Everitt, B. (1974). Cluster Analysis. London: Heinemann Educ. Books.
Hartigan, J.A. (1975). Clustering Algorithms. New York: Wiley.
Sneath, P. H. A. and R. R. Sokal (1973). Numerical Taxonomy. San Francisco: Freeman.
Anderberg, M. R. (1973). Cluster Analysis for Applications. Academic Press: New York.
Gordon, A. D. (1999). Classification. Second Edition. London: Chapman and Hall / CRC
Murtagh, F. (1985). “Multidimensional Clustering Algorithms”, in COMPSTAT Lectures 4. Wuerzburg: Physica-Verlag (for algorithmic details of algorithms used).
McQuitty, L.L. (1966). Similarity Analysis by Reciprocal Pairs for Discrete and Continuous Data. Educational and Psychological Measurement, 26, 825–831. doi:10.1177/001316446602600402.
Legendre, P. and L. Legendre (2012). Numerical Ecology, 3rd English ed. Amsterdam: Elsevier Science BV.
Murtagh, Fionn and Legendre, Pierre (2014). Ward's hierarchical agglomerative clustering method: which algorithms implement Ward's criterion? Journal of Classification, 31, 274–295. doi:10.1007/s00357-014-9161-z.