MachineShop: step_kmedoids – R documentation

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step_kmedoids

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")
)

tunable.step_kmedoids(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 and Rousseeuw PJ (1990). Finding Groups in Data: An Introduction to Cluster Analysis. Wiley: New York.

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

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)

MachineShop

Machine Learning Models and Tools

v2.8.0

GPL-3

Authors

Brian J Smith [aut, cre]

Initial release

2021-04-16