document details_k_means_klaR

Author: EmilHvitfeldt

R/k_means.R

@@ -12,6 +12,7 @@
 #'
 #' - \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
 #'
 #' @param mode A single character string for the type of model. The only
 #'   possible value for this model is "partition".

R/k_means_klaR.R

@@ -0,0 +1,14 @@
+#' K-means via klaR
+#'
+#' [k_means()] creates K-Modes model. This model is intended to be used with
+#' categorical predictors. Although it will accept numeric predictors if they
+#' contain a few number of unique values. The numeric predictors will then be
+#' treated like categorical.
+#'
+#' @includeRmd man/rmd/k_means_klaR.md details
+#'
+#' @name details_k_means_klaR
+#' @keywords internal
+NULL
+
+# See inst/README-DOCS.md for a description of how these files are processed

man/details_k_means_klaR.Rd

@@ -0,0 +1,43 @@
+```{r, child = "aaa.Rmd", include = FALSE}
+```
+
+`r descr_models("k_means", "klaR")`
+
+## Tuning Parameters
+
+```{r klaR-param-info, echo = FALSE}
+defaults <- 
+  tibble::tibble(tidyclust = c("num_clusters"),
+                 default = c("no default"))
+
+param <-
+ k_means() %>% 
+  set_engine("klaR") %>% 
+  set_mode("partition") %>% 
+  make_parameter_list(defaults)
+```
+
+This model has `r nrow(param)` tuning parameters:
+
+```{r klaR-param-list, echo = FALSE, results = "asis"}
+param$item
+```
+
+## Translation from tidyclust to the original package (partition)
+
+```{r klaR-cls}
+k_means(num_clusters = integer(1)) %>% 
+  set_engine("klaR") %>% 
+  set_mode("partition") %>% 
+  translate_tidyclust()
+```
+
+## Preprocessing requirements
+
+Only categorical variables are accepted, along with numerics with few unique values.
+
+## References
+
+- Huang, Z. (1997) A Fast Clustering Algorithm to Cluster Very Large Categorical Data Sets in Data Mining. in KDD: Techniques and Applications (H. Lu, H. Motoda and H. Luu, Eds.), pp. 21-34, World Scientific, Singapore.
+
+- MacQueen, J. (1967) Some methods for classification and analysis of multivariate observations. In Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability, eds L. M. Le Cam & J. Neyman, 1, pp. 281-297. Berkeley, CA: University of California Press.

man/k_means.Rd

@@ -0,0 +1,45 @@
+
+
+
+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("klaR") %>% 
+  set_mode("partition") %>% 
+  translate_tidyclust()
+```
+
+```
+## K Means Cluster Specification (partition)
+## 
+## Main Arguments:
+##   num_clusters = integer(1)
+## 
+## Computational engine: klaR 
+## 
+## Model fit template:
+## tidyclust::.k_means_fit_klaR(data = missing_arg(), modes = missing_arg(), 
+##     modes = integer(1))
+```
+
+## Preprocessing requirements
+
+Only categorical variables are accepted, along with numerics with few unique values.
+
+## References
+
+- Huang, Z. (1997) A Fast Clustering Algorithm to Cluster Very Large Categorical Data Sets in Data Mining. in KDD: Techniques and Applications (H. Lu, H. Motoda and H. Luu, Eds.), pp. 21-34, World Scientific, Singapore.
+
+- MacQueen, J. (1967) Some methods for classification and analysis of multivariate observations. In Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability, eds L. M. Le Cam & J. Neyman, 1, pp. 281-297. Berkeley, CA: University of California Press.