ensemble.cluster.multi {bootcluster} | R Documentation |
Multi-Method Ensemble Clustering with Graph-based Consensus
Description
Implements ensemble clustering by combining multiple clustering methods (k-means, hierarchical, and spectral clustering) using a graph-based consensus approach.
Usage
ensemble.cluster.multi(
x,
k_km,
k_hc,
k_sc,
n_ref = 3,
B = 100,
hc.method = "ward.D",
dist_method = "euclidean"
)
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") |
Details
This function implements a multi-method ensemble clustering approach that: 1. Applies multiple clustering methods (k-means, hierarchical, spectral) 2. Assesses stability of each clustering through bootstrapping 3. Constructs a weighted bipartite graph representing all clusterings 4. Uses fast greedy community detection for final consensus
Value
A list containing:
- membership
Final cluster assignments from ensemble consensus
- k_consensus
Number of clusters found in consensus
- individual_results
List of results from individual clustering methods
- stability_measures
Stability measures for each method
- graph
igraph object of the ensemble graph
Examples
data(iris)
df <- iris[,1:4]
result <- ensemble.cluster.multi(df, k_km=3, k_hc=3, k_sc=3)
plot(df[,1:2], col=result$membership, pch=16)