radiant (version 0.1.95)

kmeans_clus: K-means cluster analysis

Description

K-means cluster analysis

Usage

kmeans_clus(dataset, km_vars, data_filter = "", km_hc_init = TRUE, km_dist = "sq.euclidian", km_meth = "ward.D", km_seed = 1234, km_nr_clus = 2)

Arguments

dataset

Dataset name (string). This can be a dataframe in the global environment or an element in an r_data list from Radiant

km_vars

Vector of variables to include in the analysis

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")

km_hc_init

Use centers from hier_clus as the starting point

km_dist

Distance for hier_clus

km_meth

Method for hier_clus

km_seed

Random see to use for kmeans if km_hc_init is FALSE

km_nr_clus

Number of clusters to extract

Value

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

Details

See http://vnijs.github.io/radiant/marketing/kmeans_clus.html for an example in Radiant

See Also

summary.kmeans_clus to summarize results

plot.kmeans_clus to plot results

save_membership to add cluster membership to the selected dataset

Examples

result <- kmeans_clus("shopping", c("v1:v6"))