clust_all_levels {GSSTDA}

Get clusters for all data level

Description

It performs the clustering of the samples in each of the levels. That is to say, in each interval of values of the filtering function, the samples with a value within that interval are clustered using the proposed clustering algorithm and the proposed method to determine the optimal number of clusters.

Usage

clust_all_levels(
  data,
  samp_in_lev,
  distance_type,
  clustering_type,
  linkage_type,
  optimal_clustering_mode,
  silhouette_threshold,
  num_bins_when_clustering
)

Arguments

data

Input data matrix whose columns are the individuals and rows are the features.BR cambiar nombre.

samp_in_lev

A list of character vectors with the individuals included in each of the levels (i.e. each of the intervals of the values of the filter functions). It is the output of the samples_in_levels function.

distance_type

Type of distance to be used for clustering. Choose between correlation ("correlation") and euclidean ("euclidean").

clustering_type

Type of clustering method. Choose between "hierarchical" and "PAM" (“partition around medoids”) options.

linkage_type

Linkage criteria used in hierarchical clustering. Choose between "single" for single-linkage clustering, "complete" for complete-linkage clustering or "average" for average linkage clustering (or UPGMA). Only necessary for hierarchical clustering.

optimal_clustering_mode

Method for selection optimal number of clusters. It is only necessary if the chosen type of algorithm is hierarchical. In this case, choose between "standard" (the method used in the original mapper article) or "silhouette". In the case of the PAM algorithm, the method will always be "silhouette". "silhouette".

silhouette_threshold

Minimum value of \overline{s} that a set of clusters must have to be chosen as optimal. Within each interval of the filter function, the average silhouette values \overline{s} are computed for all possible partitions from $2$ to $n-1$, where $n$ is the number of samples within a specific interval. The $n$ that produces the highest value of \overline{s} and that exceeds a specific threshold is selected as the optimum number of clusters. If no partition produces an \overline{s} exceeding the chosen threshold, all samples are then assigned to a unique cluster. The default value is $0.25$. The threshold of $0.25$ for \overline{s} has been chosen based on standard practice, recognizing it as a moderate value that reflects adequate separation and cohesion within clusters.

num_bins_when_clustering

Number of bins to generate the histogram employed by the standard optimal number of cluster finder method. Parameter not necessary if the "optimal_clust_mode" option is "silhouette" or the "clust_type" is "PAM".

Value

List of interger vectors. Each of the vectors contains information about the nodes at each level and the individuals contained in them. The names of the vector values are the names of the samples and the vector values are the node number of that level to which the individual belongs.