document details_k_means_clustMixType

Author: EmilHvitfeldt

R/k_means.R

@@ -13,6 +13,7 @@
 #' - \link[=details_k_means_stats]{stats}: Classical K-means
 #' - \link[=details_k_means_ClusterR]{ClusterR}: Classical K-means
 #' - \link[=details_k_means_klaR]{klaR}: K-Modes
+#' - \link[=details_k_means_clustMixType]{clustMixType}: K-prototypes
 #'
 #' @param mode A single character string for the type of model. The only
 #'   possible value for this model is "partition".

R/k_means_clustMixType.R

@@ -0,0 +1,15 @@
+#' K-means via clustMixType
+#'
+#' [k_means()] creates K-prototypes model. A K-prototypes is the middle ground
+#' between a K-means and K-modes model, in the sense that it can be used with
+#' data that contains both numeric and categorical predictors.
+#'
+#' Both numeric and categorical predictors are requires for this engine.
+#'
+#' @includeRmd man/rmd/k_means_clustMixType.md details
+#'
+#' @name details_k_means_clustMixType
+#' @keywords internal
+NULL
+
+# See inst/README-DOCS.md for a description of how these files are processed

man/details_k_means_clustMixType.Rd

@@ -0,0 +1,45 @@
+```{r, child = "aaa.Rmd", include = FALSE}
+```
+
+`r descr_models("k_means", "clustMixType")`
+
+## Tuning Parameters
+
+```{r clustMixType-param-info, echo = FALSE}
+defaults <- 
+  tibble::tibble(tidyclust = c("num_clusters"),
+                 default = c("no default"))
+
+param <-
+ k_means() %>% 
+  set_engine("clustMixType") %>% 
+  set_mode("partition") %>% 
+  make_parameter_list(defaults)
+```
+
+This model has `r nrow(param)` tuning parameters:
+
+```{r clustMixType-param-list, echo = FALSE, results = "asis"}
+param$item
+```
+
+## Translation from tidyclust to the original package (partition)
+
+```{r clustMixType-cls}
+k_means(num_clusters = integer(1)) %>% 
+  set_engine("clustMixType") %>% 
+  set_mode("partition") %>% 
+  translate_tidyclust()
+```
+
+## 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.

man/k_means.Rd

@@ -0,0 +1,48 @@
+
+
+
+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)
+
+
+```r
+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.