details_hier_clust_stats {tidyclust}R Documentation

Hierarchical (Agglomerative) Clustering via stats

Description

hier_clust() creates Hierarchical (Agglomerative) Clustering model.

Details

For this engine, there is a single mode: partition

Tuning Parameters

This model has 1 tuning parameters:

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 fit(), tidyclust will convert factor columns to indicators.

References


[Package tidyclust version 0.2.3 Index]