Cluster terms based on their similarity matrix

Usage

cluster_terms(
  mat,
  method = "binary_cut",
  control = list(),
  verbose = se_opt$verbose
)

cluster_by_kmeans(mat, max_k = max(2, min(round(nrow(mat)/5), 100)), ...)

cluster_by_pam(mat, max_k = max(2, min(round(nrow(mat)/10), 100)), ...)

cluster_by_dynamicTreeCut(mat, minClusterSize = 5, ...)

cluster_by_fast_greedy(mat, ...)

cluster_by_leading_eigen(mat, ...)

cluster_by_louvain(mat, ...)

cluster_by_walktrap(mat, ...)

cluster_by_mclust(mat, G = seq_len(max(2, min(round(nrow(mat)/5), 100))), ...)

cluster_by_apcluster(mat, s = apcluster::negDistMat(r = 2), ...)

cluster_by_hdbscan(mat, minPts = 5, ...)

cluster_by_MCL(mat, addLoops = TRUE, ...)

Arguments

mat: A similarity matrix.
method: The clustering methods. Value should be in all_clustering_methods().
control: A list of parameters passed to the corresponding clustering function.
verbose: Whether to print messages.
max_k: Maximal k for k-means/PAM clustering. K-means/PAM clustering is applied from k = 2 to k = max_k.
...: Other arguments.
minClusterSize: Minimal number of objects in a cluster. Pass to dynamicTreeCut::cutreeDynamic().
G: Passed to the G argument in mclust::Mclust() which is the number of clusters.
s: Passed to the s argument in apcluster::apcluster().
minPts: Passed to the minPts argument in dbscan::hdbscan().
addLoops: Passed to the addLoops argument in MCL::mcl().

Value

A vector of numeric cluster labels.

Details

New clustering methods can be registered by register_clustering_methods().

Please note it is better to directly use cluster_terms() for clustering while not the individual cluster_by_* functions because cluster_terms() does additional cluster label adjustment.

By default, there are the following clustering methods and corresponding clustering functions:

kmeans see cluster_by_kmeans().
dynamicTreeCut see cluster_by_dynamicTreeCut().
mclust see cluster_by_mclust().
apcluster see cluster_by_apcluster().
hdbscan see cluster_by_hdbscan().
fast_greedy see cluster_by_fast_greedy().
louvain see cluster_by_louvain().
walktrap see cluster_by_walktrap().
MCL see cluster_by_MCL().
binary_cut see binary_cut().

The additional argument in individual clustering functions can be set with the control argument in cluster_terms().

cluster_by_kmeans(): The best k for k-means clustering is determined according to the "elbow" or "knee" method on the distribution of within-cluster sum of squares (WSS) on each k. All other arguments are passed from ... to stats::kmeans().

cluster_by_pam(): PAM is applied by fpc::pamk() which can automatically select the best k. All other arguments are passed from ... to fpc::pamk().

cluster_by_dynamicTreeCut(): All other arguments are passed from ... to dynamicTreeCut::cutreeDynamic().

cluster_by_fast_greedy(): All other arguments are passed from ... to igraph::cluster_fast_greedy().

cluster_by_leading_eigen(): All other arguments are passed from ... to igraph::cluster_leading_eigen().

cluster_by_louvain(): All other arguments are passed from ... to igraph::cluster_louvain().

cluster_by_walktrap(): All other arguments are passed from ... to igraph::cluster_walktrap().

cluster_by_mclust(): All other arguments are passed from ... to mclust::Mclust().

cluster_by_apcluster(): All other arguments are passed from ... to apcluster::apcluster().

cluster_by_hdbscan(): All other arguments are passed from ... to dbscan::hdbscan().

cluster_by_MCL(): All other arguments are passed from ... to MCL::mcl().