ensemble_metrics_bootstrap

Typically we will call ensemble_metrics with varying numbers of ranks to
consider and this function will sum up the ranks from those calls.

Density-based clustering methods are well adapted to the clustering of high-dimensional data and enable the discovery of core groups of various shapes despite large amounts of noise. This package provides a novel density-based cluster extraction method, OPTICS k-Xi, and a framework to compare k-Xi models using distance-based metrics to investigate datasets with unknown number of clusters. The vignette first introduces density-based algorithms with simulated datasets, then presents and evaluates the k-Xi cluster extraction method. Finally, the models comparison framework is described and experimented on 2 genetic datasets to identify groups and their discriminating features. The k-Xi algorithm is a novel OPTICS cluster extraction method that specifies directly the number of clusters and does not require fine-tuning of the steepness parameter as the OPTICS Xi method. Combined with a framework that compares models with varying parameters, the OPTICS k-Xi method can identify groups in noisy datasets with unknown number of clusters. Results on summarized genetic data of 1,200 patients are in Charlon T. (2019) <doi:10.13097/archive-ouverte/unige:161795>.

Thomas Charlon

opticskxi

OPTICS K-Xi Density-Based Clustering

ensemble_metrics_bootstrap function

<dl><dt>l_ensemble_metrics</dt>
<dd>Output of function ensemble_metrics</dd>
<dt>n_models</dt>
<dd>Number of best models to return</dd></dl>

Arguments

Select models based on ensemble metrics — ensemble_metrics_bootstrap

<dl>

<dt>l_ensemble_metrics</dt>
<dd>Output of function ensemble_metrics</dd>


<dt>n_models</dt>
<dd>Number of best models to return</dd>

</dl>

ensemble_metrics_bootstrap: Select models based on ensemble metrics

Description

Usage

Value

Arguments