kclus: K-clustering

In radiant-rstats/radiant.multivariate: Multivariate Menu for Radiant: Business Analytics using R and Shiny

K-clustering

Description

K-clustering

Usage

kclus(
  dataset,
  vars,
  fun = "kmeans",
  hc_init = TRUE,
  distance = "sq.euclidian",
  method = "ward.D",
  seed = 1234,
  nr_clus = 2,
  standardize = TRUE,
  lambda = NULL,
  data_filter = "",
  envir = parent.frame()
)

Arguments

dataset	Dataset
vars	Vector of variables to include in the analysis
fun	Use either "kmeans" or "kproto" for clustering
hc_init	Use centers from hclus as the starting point
distance	Distance for hclus
method	Method for hclus
seed	Random see to use for k-clustering if hc_init is FALSE
nr_clus	Number of clusters to extract
standardize	Standardize data (TRUE or FALSE)
lambda	Parameter > 0 to trade off between Euclidean distance of numeric variables and simple matching coefficient between categorical variables. Also a vector of variable specific factors is possible where the order must correspond to the order of the variables in the data. In this case all variables' distances will be multiplied by their corresponding lambda value.
data_filter	Expression entered in, e.g., Data > View to filter the dataset in Radiant. The expression should be a string (e.g., "price > 10000")
envir	Environment to extract data from

Details

See https://radiant-rstats.github.io/docs/multivariate/kclus.html for an example in Radiant

Value

A list of all variables used in kclus as an object of class kclus

See Also

summary.kclus to summarize results

plot.kclus to plot results

store.kclus to add cluster membership to the selected dataset

Examples

kclus(shopping, c("v1:v6"), nr_clus = 3) %>% str()

radiant-rstats/radiant.multivariate documentation built on Nov. 29, 2023, 9:52 p.m.