k_clusters: k-clusters model

In citrus: Customer Intelligence Tool for Rapid Understandable Segmentation

k-clusters model

Description

k-clusters method for segmentation. It can handle segmentation for both numerical data types only, by using k-means algorithm, and mixed data types (numerical and categorical) by using k-prototypes algorithm

Usage

k_clusters(data, hyperparameters, verbose = TRUE)

Arguments

data	data.frame, the data to segment
hyperparameters	list of hyperparameters to pass. They include centers: number of clusters or a set of initial (distinct) cluster centers, or 'auto'. When 'auto' is chosen, the number of clusters is optimised; iter_max: the maximum number of iterations allowed; n_start: how many random sets of cluster centers should be tried; max_centers: maximum number of clusters when 'auto' option is selected for the centers; segmentation_variables: the columns to use to segment on. standardize: whether to standardize numeric columns.
verbose	logical whether information about the clustering procedure should be given.

Value

A class called "k-clusters" containing a list of the model definition, the hyper-parameters, a table of outliers, the elbow plot (ggplot object) used to determine the optimal no. of clusters, and a lookup table containing segment predictions for customers.

citrus documentation built on June 17, 2022, 5:06 p.m.