opticskxi

For each largest distance differences on the OPTICS profile,
consecutive observations left and right on the OPTICS profile
(i.e. lower and higher OPTICS id) will be assigned to 2 different clusters
if their distance is below the distance of the edge point.
If above, observations are NA.
The pts parameter defines a minimum number of observations to form a
valley (i.e. cluster). If the number of observations in one valley is
smaller than pts, observations are set to NA.

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

OPTICS K-Xi Density-Based Clustering

opticskxi function

<dl><dt>optics_obj</dt>
<dd>Data frame returned by optics</dd>
<dt>n_xi</dt>
<dd>Number of clusters to define</dd>
<dt>pts</dt>
<dd>Minimum number of points per clusters</dd>
<dt>max_loop</dt>
<dd>Maximum iterations to find n_xi clusters</dd>
<dt>verbose</dt>
<dd>Print the ids of the largest difference considered and
cluster information if they define one</dd></dl>

Arguments

OPTICS k-Xi clustering algorithm — opticskxi

<dl>

<dt>optics_obj</dt>
<dd>Data frame returned by optics</dd>


<dt>n_xi</dt>
<dd>Number of clusters to define</dd>


<dt>pts</dt>
<dd>Minimum number of points per clusters</dd>


<dt>max_loop</dt>
<dd>Maximum iterations to find n_xi clusters</dd>


<dt>verbose</dt>
<dd>Print the ids of the largest difference considered and
cluster information if they define one</dd>

</dl>

opticskxi: OPTICS k-Xi clustering algorithm

Description

Usage

Value

Arguments

See Also

Examples