man/rmd/hier_clust_stats.md

In EmilHvitfeldt/celery: A Common API to Clustering

For this engine, there is a single mode: partition

Tuning Parameters

This model has 1 tuning parameters:

• num_clusters: # Clusters (type: integer, default: no default)

Translation from tidyclust to the original package (partition)

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

## Hierarchical Clustering Specification (partition)
## 
## Main Arguments:
##   num_clusters = integer(1)
##   linkage_method = complete
## 
## Computational engine: stats 
## 
## Model fit template:
## tidyclust::.hier_clust_fit_stats(data = missing_arg(), num_clusters = integer(1), 
##     linkage_method = "complete")

Preprocessing requirements

Factor/categorical predictors need to be converted to numeric values (e.g., dummy or indicator variables) for this engine. When using the formula method via \code{\link[=fit.cluster_spec]{fit()}}, tidyclust will convert factor columns to indicators.

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.

EmilHvitfeldt/celery documentation built on Jan. 31, 2025, 7:04 p.m.