normalize

Normalize matrix rows using given norm. Copied from text2vec package.

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>. A short video tutorial can be found at <https://www.youtube.com/watch?v=P2XAjqI5Lc4/>.

Thomas Charlon

opticskxi

OPTICS K-Xi Density-Based Clustering

normalize function

<dl><dt>m</dt>
<dd><code>matrix</code> (sparse or dense).</dd>
<dt>norm</dt>
<dd><code>character</code> the method used to normalize term vectors</dd></dl>

Arguments

Matrix normalization — normalize

<dl>

<dt>m</dt>
<dd><code>matrix</code> (sparse or dense).</dd>


<dt>norm</dt>
<dd><code>character</code> the method used to normalize term vectors</dd>

</dl>

normalize: Matrix normalization

Description

Usage

Value

Arguments