nTARP_complete_solution_no_contextual_variable: Run nTARP repeatedly in a bisecting fashion (using normalized...

In nTARP: Cluster Analysis Using Thresholding After Random Projections (n-TARP)

Run nTARP repeatedly in a bisecting fashion (using normalized within sum of squares)

Description

#' @keywords internal

Usage

nTARP_complete_solution_no_contextual_variable(
  data,
  number_of_projections,
  withinss_threshold,
  ids,
  minimum_cluster_size_percent
)

Arguments

data	Numeric matrix — dataset to be clustered using 'nTARP'
number_of_projections	Numeric — number of random projections for 'nTARP' to try for each run (usually 1000 to start)
withinss_threshold	Numeric — maximum value defining what a "quality cluster" is, based on the solution's normalized within-cluster sum of squares (typically 0.36)
ids	Numeric or character vector — identifying labels for individuals in the clusters
minimum_cluster_size_percent	Numeric — minimum size allowable for a cluster to be further bisected (as a percentage)

Details

Repeatedly applies 'nTARP' to iteratively bisect a dataset until a minimum cluster size threshold is reached, using within-cluster compactness to select optimal splits.

At each step, the algorithm evaluates candidate splits based on the normalized within-cluster sum of squares (WSS). The split that minimizes normalized WSS is retained.

The process continues recursively, bisecting the largest eligible cluster, until no resulting cluster meets the user-defined minimum size threshold.

Value

A list containing: (1) Complete solutions (i.e., outputs from the 'nTARP' function), (2) Clusters with the best gains identified using the 'pull_best_solution_and_gain' function, (3) Within-cluster sum of squares for each solution

nTARP documentation built on March 20, 2026, 5:09 p.m.