nice_palette

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

nice_palette function

<dl><dt>groups</dt>
<dd>Vector, each unique value will get a color</dd>
<dt>rainbow</dt>
<dd>If TRUE, rainbow-like colors, else differentiate successive
values</dd></dl>

Arguments

Nice palette — nice_palette

<dl>

<dt>groups</dt>
<dd>Vector, each unique value will get a color</dd>


<dt>rainbow</dt>
<dd>If TRUE, rainbow-like colors, else differentiate successive
values</dd>

</dl>

nice_palette: Nice palette

Description

Usage

Value

Arguments