snf_scheme: SNF schemes

In metasnf: Meta Clustering with Similarity Network Fusion

SNF schemes

Description

These functions manage the way in which input data frames are passed into SNF to yield a final fused network.

Usage

two_step_merge(
  dl,
  k = 20,
  alpha = 0.5,
  t = 20,
  cnt_dist_fn,
  dsc_dist_fn,
  ord_dist_fn,
  cat_dist_fn,
  mix_dist_fn,
  weights_row
)

domain_merge(
  dl,
  cnt_dist_fn,
  dsc_dist_fn,
  ord_dist_fn,
  cat_dist_fn,
  mix_dist_fn,
  weights_row,
  k,
  alpha,
  t
)

individual(
  dl,
  k = 20,
  alpha = 0.5,
  t = 20,
  cnt_dist_fn,
  dsc_dist_fn,
  ord_dist_fn,
  cat_dist_fn,
  mix_dist_fn,
  weights_row
)

Arguments

dl	A nested list of input data from data_list().
k	k hyperparameter.
alpha	alpha/eta/sigma hyperparameter.
t	SNF number of iterations hyperparameter.
cnt_dist_fn	distance metric function for continuous data.
dsc_dist_fn	distance metric function for discrete data.
ord_dist_fn	distance metric function for ordinal data.
cat_dist_fn	distance metric function for categorical data.
mix_dist_fn	distance metric function for mixed data.
weights_row	data frame row containing feature weights.

Details

individual: The "vanilla" scheme - does distance matrix conversions of each input data frame separately before a single call to SNF fuses them into the final fused network.

domain_merge: Given a data list, returns a new data list where all data objects of a particlar domain have been concatenated.

two_step_merge: Individual data frames into individual similarity matrices into one fused network per domain into one final fused network.

metasnf documentation built on April 3, 2025, 5:40 p.m.