pick_best_cluster_simon {immunaut} | R Documentation |
Select the Best Clustering Based on Weighted Scores: AUROC, Modularity, and Silhouette
Description
This function selects the optimal clustering configuration from a list of t-SNE
clustering results
by evaluating each configuration's AUROC, modularity, and silhouette scores. These scores are combined
using a weighted average, allowing for a more comprehensive assessment of each configuration's relevance.
Usage
pick_best_cluster_simon(dataset, tsne_clust, tsne_calc, settings)
Arguments
dataset |
A data frame representing the original dataset, where each observation will be assigned cluster labels
from each clustering configuration in |
tsne_clust |
A list of clustering results from different t-SNE configurations, with each element containing
|
tsne_calc |
An object containing t-SNE results on |
settings |
A list of settings for machine learning model training and scoring, including:
|
Details
For each clustering configuration in tsne_clust
, this function:
Assigns cluster labels to the dataset.
Trains machine learning models specified in
settings
on the dataset with cluster labels.Evaluates each model based on AUROC, modularity, and silhouette scores.
Selects the clustering configuration with the highest weighted average score as the best clustering result.
Value
A list containing the best clustering configuration (with the highest weighted score) and its associated information.