Apply a clustering algorithm recursively to a given time course.
reconstruct_recursive(
readouts,
method = "kmedoids",
sim = MultIS::get_similarity_matrix(readouts = readouts, upper = TRUE),
split_similarity = 0.7,
combine_similarity = 0.9,
use_silhouette = TRUE,
cluster_obj = FALSE
)The time course for which to find clusters.
Either "kmedoids", "kmeans" or any string permitted as a method for stats::hclust.
A similarity matrix used with all methods except "kmeans".
Similarity Threshold. If any two elements within a cluster are below this threshold, another split is initiated.
After Splitting, a combination phase is activated. If any two elements between two clusters have a similarity higher than this threshold, the cluster are combined.
If TRUE, silhouette is used to define number of cluster during splitting, otherwise cluster are always split into two new clusters.
If TRUE, a clusterObject with the readouts, similarity and clustering is returned.
A matrix with two columns: "Clone" and "IS" or if cluster_obj = TRUE a cluster object, which can be used to plot the clustering.