Consensus Clustering

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Documentation for package ‘ConsensusClustering’ version 1.2.0

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adj_conv Convert adjacency function to the affinity matrix
adj_mat Covert data matrix to adjacency matrix
CC_cluster_count Count the number of clusters based on stability score.
cluster_relabel Relabeling clusters based on cluster similarities
coCluster_matrix Calculate the Co-cluster matrix for a given set of clustering results.
connectivity_matrix Build connectivity matrix
consensus_matrix Calculate consensus matrix for data perturbation consensus clustering
gaussian_clusters Generate clusters of data points from Gaussian distribution with randomly generated parameters
gaussian_clusters_with_param Generate clusters of data points from Gaussian distribution with given parameters
gaussian_mixture_clusters Generate clusters of data points from Gaussian-mixture-model distributions with randomly generated parameters
generate_gaussian_data Generate a set of data points from Gaussian distribution
hir_clust_from_adj_mat Hierarchical clustering from adjacency matrix
indicator_matrix Build indicator matrix
lebel_similarity Similarity between different clusters
Logit Logit function
majority_voting Consensus mechanism based on majority voting
multiview_clusters Generate multiview clusters from Gaussian distributions with randomly generated parameters
multiview_cluster_gen Multiview cluster generation
multiview_consensus_matrix Calculate consensus matrix for multi-data consensus clustering
multiview_kmeans_gen Multiview K-means generation
multiview_pam_gen Multiview PAM (K-medoids) generation
multi_cluster_gen Multiple cluster generation
multi_kmeans_gen Multiple K-means generation
multi_pam_gen Multiple PAM (K-medoids) generation
pam_clust_from_adj_mat PAM (k-medoids) clustering from adjacency matrix
spect_clust_from_adj_mat Spectral clustering from adjacency matrix