bootcluster (version 0.4.2)

ensemble_cluster_multi_combinations: Multi-Method Ensemble Clustering with Multiple Stability Combinations

Description

Implements ensemble clustering using multiple methods for combining stability measures, generating separate consensus results for each combination method.

Usage

ensemble_cluster_multi_combinations(
  x,
  k_km,
  k_hc,
  k_sc,
  n_ref = 3,
  B = 100,
  hc.method = "ward.D",
  dist_method = "euclidean",
  alpha = 0.25
)

Value

A list containing results for each combination method:

product

Results using product combination

arithmetic

Results using arithmetic mean

geometric

Results using geometric mean

harmonic

Results using harmonic mean

weighted

Results using weighted combination

Each method's results contain:

fastgreedy

Results from fast greedy community detection

metis

Results from METIS (leading eigenvector) community detection

hmetis

Results from hMETIS (Louvain) community detection

graph

igraph object of the ensemble graph

edge_weights

Edge weights of the graph

individual_results

Results from individual clustering methods

stability_measures

Stability measures

incidence_matrix

Incidence matrix used for graph construction

Each community detection method's results contain:

membership

Final cluster assignments

k_consensus

Number of clusters found

The function also returns comparison statistics for each community detection method:

comparison$fastgreedy

Comparison stats for fast greedy results

comparison$metis

Comparison stats for METIS results

comparison$hmetis

Comparison stats for hMETIS results

Arguments

x

data.frame or matrix where rows are observations and columns are features

k_km

number of clusters for k-means clustering

k_hc

number of clusters for hierarchical clustering

k_sc

number of clusters for spectral clustering

n_ref

number of reference distributions for stability assessment (default: 3)

B

number of bootstrap samples for stability estimation (default: 100)

hc.method

hierarchical clustering method (default: "ward.D")

dist_method

distance method for spectral clustering (default: "euclidean")

alpha

weight for weighted combination (default: 0.5)

Examples

# \donttest{
data(iris)
df <- iris[,1:4]
results <- ensemble_cluster_multi_combinations(df, k_km=3, k_hc=3, k_sc=3)
# Compare cluster assignments from different methods
table(product = results$product$membership, 
      arithmetic = results$arithmetic$membership)
# }