man/rmd/k_means_clustMixType.md

In tidyclust: 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)

k_means(num_clusters = integer(1)) %>% 
  set_engine("clustMixType") %>% 
  set_mode("partition") %>% 
  translate_tidyclust()

## K Means Cluster Specification (partition)
## 
## Main Arguments:
##   num_clusters = integer(1)
## 
## Computational engine: clustMixType 
## 
## Model fit template:
## tidyclust::.k_means_fit_clustMixType(x = missing_arg(), k = missing_arg(), 
##     keep.data = missing_arg(), k = integer(1), keep.data = TRUE, 
##     verbose = FALSE)

Preprocessing requirements

Both categorical and numeric predictors are required.

References

• Szepannek, G. (2018): clustMixType: User-Friendly Clustering of Mixed-Type Data in R, The R Journal 10/2, 200-208, doi:10.32614/RJ-2018-048.

• Aschenbruck, R., Szepannek, G., Wilhelm, A. (2022): Imputation Strategies for Clustering Mixed‑Type Data with Missing Values, Journal of Classification, doi:10.1007/s00357-022-09422-y.

• Z.Huang (1998): Extensions to the k-Means Algorithm for Clustering Large Data Sets with Categorical Variables, Data Mining and Knowledge Discovery 2, 283-304.

tidyclust documentation built on Sept. 26, 2023, 1:08 a.m.