ensemble_cluster_multi_combinations {bootcluster} | R Documentation |
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
)
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) |
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$hmetis
Comparison stats for hMETIS results
Examples
data(iris)
df <- iris[,1:4]
results <- ensemble_cluster_multi_combinations(df, k_km=3, k_hc=3, k_sc=3)
# Compare cluster assignments from different methods
table(product = results$product$membership,
arithmetic = results$arithmetic$membership)