step_kmedoids: K-Medoids Clustering Variable Selection

In brian-j-smith/MachineShop: Machine Learning Models and Tools

K-Medoids Clustering Variable Selection

Description

Creates a specification of a recipe step that will partition numeric variables according to k-medoids clustering and select the cluster medoids.

Usage

step_kmedoids(
  recipe,
  ...,
  k = 5,
  center = TRUE,
  scale = TRUE,
  method = c("pam", "clara"),
  metric = "euclidean",
  optimize = FALSE,
  num_samp = 50,
  samp_size = 40 + 2 * k,
  replace = TRUE,
  prefix = "KMedoids",
  role = "predictor",
  skip = FALSE,
  id = recipes::rand_id("kmedoids")
)

## S3 method for class 'step_kmedoids'
tunable(x, ...)

Arguments

recipe	recipe object to which the step will be added.
...	one or more selector functions to choose which variables will be used to compute the components. See selections for more details. These are not currently used by the tidy method.
k	number of k-medoids clusterings of the variables. The value of k is constrained to be between 1 and one less than the number of original variables.
center, scale	logicals indicating whether to mean center and median absolute deviation scale the original variables prior to cluster partitioning, or functions or names of functions for the centering and scaling; not applied to selected variables.
method	character string specifying one of the clustering methods provided by the cluster package. The clara (clustering large applications) method is an extension of pam (partitioning around medoids) designed to handle large datasets.
metric	character string specifying the distance metric for calculating dissimilarities between observations as "euclidean", "manhattan", or "jaccard" (clara only).
optimize	logical indicator or 0:5 integer level specifying optimization for the pam clustering method.
num_samp	number of sub-datasets to sample for the clara clustering method.
samp_size	number of cases to include in each sub-dataset.
replace	logical indicating whether to replace the original variables.
prefix	if the original variables are not replaced, the selected variables are added to the dataset with the character string prefix added to their names; otherwise, the original variable names are retained.
role	analysis role that added step variables should be assigned. By default, they are designated as model predictors.
skip	logical indicating whether to skip the step when the recipe is baked. While all operations are baked when prep is run, some operations may not be applicable to new data (e.g. processing outcome variables). Care should be taken when using skip = TRUE as it may affect the computations for subsequent operations.
id	unique character string to identify the step.
x	step_kmedoids object.

Details

K-medoids clustering partitions variables into k groups such that the dissimilarity between the variables and their assigned cluster medoids is minimized. Cluster medoids are then returned as a set of k variables.

Value

Function step_kmedoids creates a new step whose class is of the same name and inherits from step_sbf, adds it to the sequence of existing steps (if any) in the recipe, and returns the updated recipe. For the tidy method, a tibble with columns terms (selectors or variables selected), cluster assignments, selected (logical indicator of selected cluster medoids), silhouette (silhouette values), and name of the selected variable names.

References

Kaufman, L., & Rousseeuw, P. J. (1990). Finding groups in data: An introduction to cluster analysis. Wiley.

Reynolds, A., Richards, G., de la Iglesia, B., & Rayward-Smith, V. (1992). Clustering rules: A comparison of partitioning and hierarchical clustering algorithms. Journal of Mathematical Modelling and Algorithms, 5, 475-504.

See Also

pam, clara, recipe, prep, bake

Examples

library(recipes)

rec <- recipe(rating ~ ., data = attitude)
kmedoids_rec <- rec %>%
  step_kmedoids(all_predictors(), k = 3)
kmedoids_prep <- prep(kmedoids_rec, training = attitude)
kmedoids_data <- bake(kmedoids_prep, attitude)

pairs(kmedoids_data, lower.panel = NULL)

tidy(kmedoids_rec, number = 1)
tidy(kmedoids_prep, number = 1)

brian-j-smith/MachineShop documentation built on Sept. 22, 2023, 10:01 p.m.